medical data understanding: an - · pdf filemedical data understanding: an overview dr. h s...

Proceedings of the seminar on “Recent tools for Dimensionality Reduction in Understanding Medical Data” – 22nd

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Medical Data Understanding: An

Overview

Dr. H S Nagedraswamy

Professor, DOS in Computer Science Manasagangothri, Mysore-560007

1. Introduction

With the advancement of science and technology, automation took place

in various sectors such as banking, business, education, medicine,

agriculture etc. The major goal of any automation task is to minimize

the effort, maximize the productivity and to enhance the quality of

service. In the field of medicine, automation systems such as intelligent

experts systems and decision support systems help physicians and

medical practitioners effectively diagnose the diseases and make right

decisions in treating a patient. In order to design expert systems or

decision support systems, a huge volume of heterogeneous data of

possibly high dimension need to be gathered, pre-processed,

represented, analyzed and interpreted. So, from the automation point of

view, understanding medical data is very much important for

professionals who design experts systems as well as for physicians who

validate the designed system.

2. Medical Data and its Importance

Medical datum is any single observation of a patient - for example, a

temperature reading, a red-blood-cell count, a past history of rubella, or

a blood-pressure reading. Data provide the basis for categorizing the

problems a patient may be having, or for identifying subgroups within a


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population of patients. They also help a physician to decide what

additional information is needed and what actions should be taken to

gain a greater understanding of a patient’s problem or to treat most

effectively the problem that has been diagnosed.

Types of Medical Data

The types of medical data in the practice of medicine and the allied

health sciences include narrative textual data to numerical

measurements, recorded signals, drawings, and even photographs.

Some narrative data are loosely coded with shorthand conventions

known to health personnel, particularly data collected during the

physical examination, in which recorded observations reflect the

stereotypic examination process taught to all practitioners. It is

common, for example, to find the notation “PERRLA” under the eye

examination in a patient’s medical record. This encoded form indicates

that the patient’s “Pupils are Equal (in size), Round, and Reactive to

Light and Accommodation”.

Many data used in medicine take on discrete numeric values. These

include such parameters as laboratory tests, vital signs (such as

temperature and pulse rate), and certain measurements taken during

the physical examination. When such numerical data are interpreted,

however, the issue of precision becomes important. Can a physician

distinguish reliably between a 9-cm and a 10-cm liver span when she

examines the patient’s abdomen? Does it make sense to report a serum

sodium level to two-decimal-place accuracy? Is a 1-kg fluctuation in

weight from one week to the next significant? Was the patient weighed

on the same scale both times (that is, could the different values reflect


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variation between measurement instruments rather than changes in the

patient)?

In some fields of medicine, analog data in the form of continuous

signals are particularly important. The best known example is an

electrocardiogram (ECG), a tracing of the electrical activity from a

patient’s heart. When such data are stored in medical records, a

graphical tracing frequently is included, with a written interpretation of

its meaning. There are clear challenges in determining how such data

are best managed in computer storage systems.

Visual images are another important category of data, which are either

acquired from machines or sketched by the physician. Radiologic

images are obvious examples of this type. It also is common for a

physician to draw simple pictures to represent abnormalities that

she/he has observed; such drawings may serve as a basis for

comparison when she or another physician next sees the patient. For

example, a sketch is a concise way of conveying the location and size of

a nodule in the prostate gland.

3. Data Measurement

Precise measurement of medical data is very much important, which

would otherwise leads to wrong conclusion. Medical data are multiple

observations about a patient. A single datum generally viewed as

defined by four elements:

• The patient in question.

• The parameter being observed (for example, liver size, urine-

sugar value, history of rheumatic fever, heart size on chest X-ray

film).


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• The value of the parameter in question (for example, weight is 70

kg, temperature is 98.6o F, profession is steel worker).

• The time of the observation (for example, 2:30 A.M. on 14 FEB

2013).

It is important to keep a record of the circumstances under which a

datum was obtained. For example,

• Was the blood pressure taken in the arm or leg?

• Was the patient lying or standing?

• Was it obtained just after exercise?

• During sleep?

• What kind of recording device was used?

• Was the observer reliable?

It is rare that an observation – even by a skilled clinician - can be

accepted with absolute certainty. A related issue is the uncertainty in

the values of data.

• An adult patient reports a childhood illness with fevers and a red

rash in addition to joint swelling. Could he have had scarlet fever?

The patient does not know what his pediatrician called the disease.

• A physician listens to the heart of an asthmatic child and thinks that

she hears a heart murmur— but she is not certain because of the

patient’s loud wheezing.

• A radiologist looking at a shadow on a chest X-ray film is not sure

whether it represents overlapping blood vessels or a lung tumor.

• A confused patient is able to respond to simple questions about his

illness, but his physician is uncertain how much of his reported

history is reliable.

4. Uses of Medical Data


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Medical data are recorded for a variety of purposes. They may be needed

to support the proper care of the patient from whom they were obtained.

They also may contribute to the good of society through the aggregation

and analysis of data regarding populations of individuals.

Create the Basis for the Historical Record

What is the patient’s history (development of a current

illness; other diseases that coexist or have resolved;

pertinent family, social, and demographic information)?

What symptoms has the patient reported?

What physical signs have been noted on examination?

How have signs and symptoms changed over time?

What laboratory results have been or are now available?

What radiologic and other special studies have been

performed?

What interventions have been undertaken?

What is the reasoning behind those management decisions?

Each new patient complaint and its management can be viewed as a

therapeutic experiment, inherently confounded by uncertainty, with the

goal

of answering three questions:

1. What was the nature of the disease or symptom?

2. What was the treatment decision?

3. What was the outcome of that treatment?

Anticipate Future Health Problems

Data gathered routinely in the ongoing care of a patient may suggest

that he is at high risk of developing a specific problem, even though he

may feel well and be without symptoms at present. Medical data

therefore are important in screening for risk factors, following patients’


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risk profiles over time, and providing a basis for specific patient

education or preventive interventions, such as diet, medication, or

exercise.

Record Standard Preventive Measures

The medical record also serves as a source of data on interventions that

have been performed to prevent common or serious disorders. The best

examples of such interventions are immunizations: the vaccinations

that begin in early childhood and may continue throughout life,

including special treatments administered when a person will be at

particularly high risk (for example, injections of gamma globulin to

protect people from hepatitis, administered before travel to areas where

hepatitis is endemic).

Identify Deviations from Expected Trends

Data often are useful in medical care only when viewed as part of a

continuum over time. An example is the routine monitoring of children

for normal growth and development by pediatricians. Single data points

regarding height and weight generally are not useful by themselves; it is

the trend in such data points observed over months or years that may

provide the first clue to a medical problem. It is accordingly common for

such parameters to be recorded on special charts or forms that make

the trends easy to discern at a glance.

Provide a Legal Record

Another use of medical data, once they are charted and analyzed, is as

the foundation for a legal record to which the courts can refer if

necessary. The medical record is a legal document; most of the clinical

information that is recorded must be signed by the responsible

individual. In addition, the chart generally should describe and justify

both the presumed diagnosis for a patient and the choice of


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management. A well maintained record is a source of protection for both

patients and their physicians.

Support Clinical Research

Another use of medical data is to support clinical research through the

aggregation and statistical analysis of observations gathered from

populations of patients.

5. Weaknesses of the Traditional Medical-Record System

The traditional way of keeping records possesses certain limitations.

Following are the few such limitations highlighted.

Pragmatic and Logistical Issues

The data cannot effectively serve the delivery of health care unless they

are recorded. Their optimal use is dependent on positive responses to

the following questions:

• Can I find the data I need when I need them?

• Can I find the medical record in which they are recorded?

• Can I find the data within the record?

• Can I find what I need quickly?

• Can I read and interpret the data once I find them?

• Can I update the data reliably with new observations in a

form consistent with the requirements for future access by myself

or other people?

Redundancy and Inefficiency

In order to be able to find data quickly in the chart, health professionals

have developed a variety of techniques that provide redundant recording

to match alternate modes of access.


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The Passive Nature of Paper Records

A manual archival system is inherently passive; the charts sit waiting

for something to be done with them. They are insensitive to the

characteristics of the data recorded within their pages, such as

legibility, accuracy, or implications for patient management.

Computational techniques for data storage, retrieval, and analysis make

it feasible to develop record systems that (1) monitor their contents and

generate warnings or advice for providers based on single observations

or on logical combinations of data; (2) provide automated quality

control, including the flagging of potentially erroneous data; or (3)

provide feedback of patient-specific or population-based deviations from

desirable standards.

6. The Structure of Medical Data

Scientific disciplines generally develop precise terminology or notation

that is standardized and accepted by all workers in the field.

Imprecision and the lack of a standardized vocabulary are particularly

problematic when we wish to aggregate data recorded by multiple

health professionals or to analyze trends over time. Without a

controlled, predefined vocabulary, data interpretation is inherently

complicated, and the automatic summarization of data may be

impossible. For example, one physician might note that a patient has

“shortness of breath.” Later, another physician might note that she has

“dyspnea.” Unless these terms are designated as synonyms, an

automated flowcharting program will fail to indicate that the patient

had the same problem on both occasions.

Coding Systems


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Because of the needs to know about health trends for populations and

to recognize epidemics in their early stages, there are various health-

reporting requirements for hospitals (as well as other public

organizations) and practitioners. For example, cases of gonorrhea,

syphilis, and tuberculosis generally must be reported to local public-

health organizations, which code the data to allow trend analyses over

time. The Centers for Disease Control (CDC) in Atlanta then pool

regional data and report national as well as local trends in disease

incidence, bacterial-resistance patterns, and the like. Researchers at

many institutions have worked for over a decade to develop a unified

medical language system (UMLS), a common structure that ties together

the various vocabularies that have been created.

The Data-to-Knowledge Spectrum

Datum as a single observational point, generally can be regarded as the

value of a specific parameter for a particular object (for example, a

patient) at a given point in time. Knowledge, then, is derived through

the formal or informal analysis (or interpretation) of data. The term

information is more generic in that it encompasses both organized

data and knowledge.

The observation that a patient Brown has a blood pressure of 180/110

is a datum, as is the report that the patient has had a myocardial

infarction (heart attack). When researchers pool and analyze such data,

they may determine that patients with high blood pressure are more

likely to have heart attacks than are patients with normal or low blood

pressure. This data analysis has produced a piece of knowledge about

the world.

7. The Computer and Collection of Medical Data


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Physicians may be asked to fill out structured paper data sheets, or

such sheets may be filled out by data abstractors who review patient

charts, but the actual entry of data into the database is done by paid

transcriptionists. In some applications, it is possible for data to be

entered automatically into the computer by the device that measures or

collects them. Certain data can be entered directly by patients; there are

systems, for example, that take the patient’s history by presenting on a

terminal multiple-choice questions that follow a branching logic. The

patient’s responses to the questions are used to generate hardcopy

reports for physicians, and also may be stored directly in a computer

database for subsequent use in other settings.

Summary

Medical data plays an important role in designing intelligent experts

systems and decision support systems. Various types of medical data

such as textual data, discrete numerical data, analog data, recorded

signals, visual images and photographs are normally used by experts

for analyzing patients. Precise measurement of medical data and

handling uncertainty associated with measurement is very much

important. Medical data are used for a wide variety of reasons.

Traditional medical records have some serious drawbacks and can be

alleviated through automation, which requires structuring of medical

data for storage, retrieval and manipulation.

References

1. Van Bemmel J.H. et al (Eds). Data, Information and Knowledge in Medicine. Methods of Information in Medicine, Special issue, 27(3), 1988.


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2. Patel V.L., Arocha J.F., Kaufman, D.R. Diagnostic reasoning and medical expertise. In Medin D. (Ed.) The psychology of learning

and motivation, 31:187-252. New York: Academic Press,1994.

3. Chute C.G., Cohn S., Campbell K.E., Oliver D., Campbell J.R. The

content coverage of clinical classifications. Journal of the American Medical Informatics Association 1996; 3(3):224-33.

4. Campbell J.R., Carpenter P., Sneiderman C., Cohn S., Chute

C.G., Warren J. Phase II evaluation of clinical coding schemes: Completeness, taxonomy, mapping, definitions, and clarity.

Journal of the American Medical Informatics Association 1997; 4(3):238-51.

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Recent Tools and Techniques for Medical Image Processing

Dr. Vinay K

Assistant Professor

PG Department of Computer Science JSS College of Arts, Commerce and Science

Ooty Road, Mysore [email protected]

1.0 INTRODUCTION

Medical imaging is the technique and process used to create images of

the human body for clinical purposes or medical science. Medical

imaging has a number of techniques which can be used as non-

intruding methods of looking into the body. This means the body does

not have to be open surgically to look at various organs and areas. It

can be used to assist diagnosis or treatment of different medical

conditions. Since from the discovery of X-rays by Wilhelm Conrad

R¨ontgen in 1895, medical images have become a major component of

diagnostics, treatment planning.

In today’s world, doctors would be able to diagnose, treat and cure

patients without surgically opening the body, also not causing harmful

side effects. The use of medical image processing has enabled doctors to

gain a more immediate and accurate understanding of a patient’s

condition than ever before without having to cut inside of the body.

Medical imaging also helps us learn more about neurobiology and

human behaviors. Medical images are used for education,

documentation, and research describing morphology as well as physical

and biological functions in 1D, 2D, 3D, and even 4D image data.

mailto:[email protected]


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Medical imaging includes different imaging modalities and processes to

image human body for diagnostic and treatment purposes and therefore

has an important role in the improvement of public health in all

population groups. Furthermore, medical imaging allows to follow the

course of a disease already diagnosed or treated. Area of medical

imaging is very complex and, depending on a context, requires

supplementary activities of medical doctors, medical physicists,

biomedical engineers as well as technicians, so cost effectiveness should

be handled and can be enhanced by more efficient data handling in the

hospitals, which has become possible through the digitization of

diagnostic information.

Medical Imaging Technology has been developed to satisfy the huge

demand for information on medical imaging, a demand made not only

by radiologists but also by cardiologists, physicians and senior

healthcare managers. The report published in 2008 on brain scan

medical image processing where brain imaging is being used to

understand why some people become long-term cocaine addicts and

some do not.

1.1 ADVANCES IN MEDICAL IMAGE PROCESSING

The field of medical imaging, influenced by advances in digital and

communication technologies, has grown tremendously in the recent

years. New imaging techniques that reveal greater anatomical detail are

available in most imaging modalities.

Medical imaging is continually evolving and advancing, all with the goal

of improving patient care. Here are the few example listed.

The migration of X-rays from film to digital files.


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The evolution of MRIs from slow and fuzzy to fast and highly

detailed.

The portability of ultrasound.

Single-chip ultrasound and High-intensity focused ultrasound

(HIFU).

DLP Hyperspectral Imaging

By applying an optical semiconductor technology

commonly used in digital color projectors to an

imaging technique which light on an array of

potential optical medical imaging applications. The

resulting hype spectral imaging system could help

reduce the risk of complications during various medical procedures and

associated liability, when performing open or endoscopic surgery, it is

often difficult to differentiate between neighboring tissues. For example,

when removing the gallbladder, it is important not to damage the

common bile duct. If we could non-invasively distinguish the bile duct

from surrounding arteries, the surgeon would know better where to cut.

Electromagnetic Acoustic Imaging

Electromagnetic acoustic imaging

(EMAI) is a new imaging technique

that uses long-wavelength RF

electromagnetic (EM) waves to

induce ultrasound emission.

Signal intensity and image

contrast have been found to

depend on spatially varying electrical conductivity of the medium in

addition to conventional acoustic properties. The resultant conductivity-

weighted ultrasound data may enhance the diagnostic performance of


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medical ultrasound in cancer and cardiovascular applications because

of the known changes in conductivity of malignancy and blood-filled

spaces.

Wafer-Scale Mega Microchip

A large developed microchip is designed to enhance medical imaging

applications. Measuring a whopping 12.8 cm square, the chip could

eventually aid in the diagnosis of cancer, enabling doctors to see the

impact of radiotherapy treatment more precisely. The wafer-scale chip

produces images that will clearly show the effects of radiation on

tumors and help doctors to detect them earlier and because it is strong,

the chip can survive many years of exposure to radiation.

3-D Metamaterial

Although ultrasound imaging is

ubiquitous in the medical field, it has

been limited by an inability to obtain

high-resolution, detailed images. By using

a 3-D metamaterial to achieve deep-

subwavelength imaging it is believe that

they can enhance ultrasound resolution

by a factor of 50. If realized, the metamaterial could be incorporated

into current ultrasound probes to capture high-resolution medical

images, thereby improving patient care.

MRI Heart Imaging

MRI Heart Imaging is used to have revolutionized

technology for imaging the beating heart. Produced

in one of the world's most powerful MRI systems,


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with power equivalent to 150,000 times Earth's magnetic field, the

images provide much higher detail than standard cardiac images. The

ultrahigh field approach also delineates clearly between blood and heart

muscle. The new method could advance the capabilities of cardiac

research and care, enabling earlier diagnosis, monitoring, and

treatment of cardiac malfunctions.

1.3 Visualization and Analysis System for Medical Imaging

As for as the system of any Computer Aided Diagnosis concerned it

widely consists of following modules. Image Formation, Enhancement,

Visualization, Analysis and Management Module. Out of these modules

some have pipelined architecture and some are parallel processing

architecture. One important module is enhancement. In this module

medical imaging algorithms are applied to extract the region of interest

(ROI), filtering the noise present in the raw image, segmenting the Voxel

of Interest (VOI). Such processing steps are carried out using many

traditional as well as advance techniques. Visualization part is also got

much importance in medical imaging system. This is because medical

images often require higher end graphic system. Since each and every

pixel in medical images are very important and there should be no

compromise in viewing system. Different perception will yield different

meaning. Hence advanced computer graphic algorithms and high end

hardware systems are required for medical image processing task.


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Figure-1: Steps and Modules in Medical Image Processing System

2.0 Recent tools in Medical image Processing

In the last decade many remarkable research have been carried out in

the field of medical image processing. Many industry and health care

standard CAD systems were designed and effectively employed in

medical services. Research and Development companies like GE and

Siemens are designing many commercial devices. Many research groups

and researchers consortium are effectively working and designing

efficient algorithm. There is numerous software which are made open

source distributed with GPL (General Public License) and are freely

available for the research purpose. ITK/VTK, ImageJ, MANGO, MIPAV

and MeVISLAB are some of the standard computer programming tools

which are freely available. Apart for these imaging software many data

mining and Patter Recognition software like PRLab, SPM toolkit,

MATLAB and R programming languages are available for medical image

analysis.

CONCLUSION


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Medical image processing is one of the fields which is rapidly

growing. Many research dedicated research labs are working in

developing sophisticated algorithm on processing medical data. As

time progress there lot many newer image modalities are evolving

and parallels advance research is also taking place in medical field.

Therefore medical image processing is one such field it never gets

saturate. Since newer modality of images involve different anatomical

structure and hence newer algorithms are required to answer such

issues.


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Dimensionality Reduction

Dr. S Manjunath

JSS College of Arts, Commerce and Science

Ooty Road, Mysore, Karnataka, India [email protected]

Now-a-days, there is a greater demand for medical data analysis and

understanding. In this process it is known that the raw medical data

(either, medical images from different modalities, medical data in the

form of text) is available in abandon. In order to understand and

analyze the raw data it is necessary to bring it into a structured format.

Normally, the simple way of representing the data is called pattern

matrix of feature matrix where rows corresponds to patters or objects or

observations and columns represents the attributes or features or

random variables. This type of representations is not free from noise,

redundant, or irrelevant data. In order to represent it in more efficient

way we have technique called as dimensionality reduction. In this

session, we are going look into basics of dimensionality reduction

techniques and tools available for dimensionality reduction.

Dimensionality reduction is a process of elimination or reduction or

transformation of random variables or features while preserving the

structure of the original data. Dimensionality reduction helps us in

solving the problem of curse of dimensionality when we have huge

number of features. Also, it provides better visualization of high

dimensional data along with being a good noise removal and data

compression technique. Dimensionality reduction techniques suffer

from possibility of information loss and recovering the original data if

they are performed poor on the original data.


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To name a few application of dimensionality reduction can be found in

the following area: medical data analysis, text categorization,

biometrics, document image processing, compression etc.

Dimensionality reduction techniques can be classified into classes two

classes viz., 1. Feature selection methods, 2 Feature Extractor methods.

In feature selection methods the original set of features are analyzed

and optimal subset of features are selected for further processing where

as in case of feature extraction methods the original set of features in

some space (say in Euclidean space) are transformed onto some other

space preserving the structure of the data and in the transformed space

the features are selected. The selection of features depends on an

objective and objective function depends on application for which

directionality reductions is being used. In order to understand

dimensionality reduction techniques let us consider there are ‘N’

number of patterns with‘d’ number of features.

The feature selection method tries to find out optimal subset of size ‘k’

out of ‘d’ number of features from ‘N’ patterns. This involves four stages:

1. Subset generation: In this step the subset of features are

generated each time. In order to generate subsets one can think

of exhaustive search, compete search, heuristic search,

probabilistic search and hybrid search methods.

2. Evaluation: The generated subset is evaluated to check whether

the generated subset meets the objective criterion or not. The

evaluation techniques can be either filter or wrapper or hybrid

approach. The filter based approaches are independent of an

inductive algorithm, whereas wrapper methods use an inductive


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algorithm as the evaluation function. The hybrid approaches are

combinations of filter and wrappers approaches.

3. Stopping Criteria: This step is responsible for stopping the

process of subset generation and evaluation steps. In this step, if

the subset of features generated in subset generation stage

satisfies the objective criteria then process of feature selection is

stopped else the process continues with stage 1 by generating a

new subset of features.

4. Validation stage: This is an optional stage, where the selected

features are validated to check whether the selected features are

good enough on a real data or not.

In feature extraction methods, a pattern di RX is transformed to k

iY

using any suitable transformation function ‘T’. In the transformed

domain analysis on projected data in carried out to select the suitable

features and it is given by ki

FunctiontionTransforma

di YRX .

Feature extraction methods can be classified as supervised (Principal

Component Analysis [1], Independent Component Analysis [2], Latent

Semantic Analysis [3]), unsupervised (Fisher Linear Discriminant

Analysis [4]) and semi-supervised approaches. In case of supervised

approaches, the information about patters is given during

transformation whereas in case of unsupervised no information about

the samples is provided. In real world, it is observed that among entire

data some data may have information and some may not have

information in such case one can think of applying semi-supervised

techniques.

In the market we can see plenty of tools for reducing the dimensionality

of data such as MatLab, Weka, R, etc. In this session demonstration on


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MatLab on different medical data such as heart diseases and lung

cancer will be discussed.

References:

1. Duda R. O, Hart P E, and Stork D G. (2002). Pattern

Classification. John Willey & Sons Publications.

2. Pierre C (1994). Independent Component Analysis: a new

concept? Signal Processing, Vol. 36, N.3, pp.287–314.

3. Kurimo, M. (1999). Indexing audio documents by using latent semantic analysis and SOM. In: Oja, Erkki and Kaski, Samuel (Ed.), Kohonen Maps, Elsevier Amsterdam, pp. 363–374.

4. Fisher R. A. (1938). The Statistical Utilization of Multiple

Measurements, Annalsof Eugenics, vol. 8, pp. 376-386.

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PAPER

PRESENTED


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An Enhanced Natural Scene Classification Based Image Browsing and

Retrieval System

Srinidhi S, Dr. Vinay K, Dr. G. Hemantha Kumar Department of Studies in Computer Science, University of Mysore

Manasagangotri-570006, Mysore, INDIA.

[email protected], [email protected]

Abstract

The objective of Natural scene classification is to classify images into

pre-defined scene categories. The number of digital images that needs to

be acquired, analyzed, classified, stored and retrieved in the scene

classification is exponentially growing. Accordingly, scene classification

and retrieval has become a popular topic in the recent years. In order to

find an image, the image has to be described or represented by certain

features. Since our problem is to classify the scene images, the

description of images using texture and shape analysis is predominant.

Texture and Shape is an important visual feature of an image. There are

many texture and shape representation and classification techniques in

the literature. In proposed algorithm, we classify and review some

important shape and texture feature extraction techniques. Finally the

performance of each feature and fusion of features are tested using k-

Nearest neighbor learning framework and Probabilistic Neural Network

learning framework.

Keywords: Natural scene classification, k-Nearest Neighbor,

Probabilistic Neural Network, texture feature and shape feature.

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1. INTRODUCTION

Natural scene classification comprises of a set of simple techniques and

procedures, which are used to work on scene images, in order to

classify the scene images.

Natural scene classification is usually divided into three stages. The

first stage acquires the scene image. The second stage extracts

features. In final stage, classification techniques are used.

Since many years, several researches have been carried out in

developing systems to facilitate natural scene classification. The current

tools for browsing in large databases are not prepared to deal with this

type of data. And moreover, the uses of natural scene classification have

shown itself an expensive option from computational point of view.

Therefore, it is necessary to set up other categories of information

retrieval systems in order to extend the treatment to poor quality scene

images. For this purpose, another system is developed which is mainly

focused on retrieval system in scene images without explicit recognition.

This system is called as “Natural scene classification”.

Natural Scene Classification System is a system for image browsing,

searching and retrieving images from a large database of digital images.

The query comprises either an actual example from the collection of

dataset. An extremely important aspect in the retrieval procedure is the

image representation which works on features. The scene classification

procedure is used in a supervised manner.

In literature, many authors have presented different techniques to

classify scene. In [1], the scene classification of the scene has been

performed by extracting Global feature and is mapped to other extracted

features. The K-SVD is applied to individual pixel and SVM classifier are

applied to whole image and determines the performance. In [2], Fast


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Fourier Transform (FFT) and GABOR filters were used to remove the

noise. GLCM and energy-based features were extracted and combined.

Individual trained SVM are applied for classification. In [3], JSEG

algorithm was introduced to segmentation of image. This model works

successfully for kth-nearest neighbor with Bayesian combination

scheme. In [4], The Independent Component Analysis (ICA) has been

taken for classifying scene. With using histogram sharpest spikes are

determined. SVM classifier is used to show better performance and good

generalization.

In this paper, we aim to develop Natural scene classification technique

for semi-scene image using features like texture and shape features,

which will be described in coming sections. Rest of the paper organized

follows: section 2 describes the proposed model, section 3 gives

experimental results and section 4 concludes the paper with the brief

summary.

2. Proposed model

The scene acquired is considered for Natural scene classification. Scene

images may contain noise: hence noise is removed by using Gabor filter.

Then feature extraction is carried out to extract features for image. The

proposed model is simple to use and understand. The architecture is as

shown in the following block model in Figure-1:


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Fig. 1: Block diagram of proposed system

2.1 Feature extraction

We extract two features from a scene image, such as texture and

shape feature which are described as follows: first extracted feature is

texture; Texture is a set of metrics calculated in image processing

designed to quantify the perceived texture of an image. Texture gives us

information about the spatial arrangement of colour or intensities in an

image or selected region of an image. Texture feature extraction like

Gabor and Gray-Level Co-occurrence Matrices (GLCM) feature

extraction is used here. for extraction of features of an image, the

system uses gabor filter. Each point is represented by local gabor filter

responses. A 2-D Gabor filter is obtained by modulating a 2-D sine wave

at particular frequencies and orientations with a Gaussian envelope. We

follow the notation in [5] [6]. The 2-D Gabor filter kernel is defined by,

iyx

yxyxyxf

kk

y

kk

x

kkk

)sincos(2exp.

)cossin()sincos(

2

1exp),,,(

2

2

2

2

Where, x and y are the standard deviations of the Gaussian envelope

along the x and y-dimensions, respectively. and k are the wavelength


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and orientation, respectively. The spread of the Gaussian envelope is

defined using the wavelength . A rotation of the x – y plane by an angle

k result in a Gabor filter at orientation k. k is defined by,

nkknk ,..,2,1)1(

Where, n denotes the number of orientations. The Gabor local feature at

a point (x, y) of an image can be viewed as the response of all different

Gabor filters located at that point. A filter response is obtained by

convolving the filter kernel (with specific, k) with the image. Here we

use Gabor kernels with 2 orientation [0.7854 1.5708] and two scales [2

5]. A co-occurrence matrix is a matrix or distribution that is defined

over an image to be the distribution of co-occurring values at a given

offset. These extracts four features namely, calculated by the formulae;

,

,

, and

.

Second feature extracted is Shape. The shape parameters like major

axis length, minor axis width, Area, Rectangularity, Eccentricity,

EulerNumber, EquivDiameter, ConvexArea, and Orientation are some

features extracted.


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2.2 Classification

Classification is a computational procedure that sorts images into

groups ("classes") according to their similarities. Images can be similar

in all kinds of ways, but in EM-related image processing we use a very

strict measure of similarity that is based on a pixel-by-pixel

comparison: the mean squared difference, generalized Euclidean

distance. Two classifiers are adopted: First, the k-Nearest Neighbor (k-

NN), k-nearest neighbor algorithm (k-NN) is a non-parametric method

for classifying objects based on closest training examples in the feature

space. There are two major design choices to make: the value of k, and

the distance function to use. When there are two alternative classes, in

order to avoid ties the most common choice for k is a small odd integer,

for example k = 3. If there are more than two classes, then ties are

possible even when k is odd. Ties can also arise when two distance

values are the same. An implementation of k-NN needs a sensible

algorithm to break ties; there is no consensus on the best way to do

this. When each example is a fixed-length vector of real numbers, the

most common distance function is Euclidean distance:

Second classification technique is Probabilistic Neural Network (PNN).

Probabilistic neural networks (PNN) are forward feed networks built with

three layers. They are derived from Bayes Decision Networks. They train

quickly since the training is done in one pass of each training vector,

rather than several. Probabilistic neural networks estimate the

probability density function for each class based on the training

samples.

3. Experimental Results and Analysis


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The experimental results are reported in terms of correctly classified

scenes of the testing dataset. The accuracy rate of Individual features

and fusion features of k-Nearest Neighbor are as shown in Table 1 and

Table 2, respectively.

k gabor glcm shape2 77.3 67.8 803 76.9 69.2 784 76.8 70.3 76.65 76.6 70.8 74.36 73.7 72.3 73.87 74.5 70.3 768 74.3 69.9 759 77.3 68.1 76.1

Table 1: Accuracy rate of Individual feature (k-NN)

k gab_glc gab_shap glc_shap all_comb2 79.3 77.4 66.7 77.43 76.9 74.8 65.6 74.84 76.7 74.6 65.3 74.65 76.6 74.4 65.3 74.46 73.7 71.2 65.6 71.37 74.5 72.2 65.9 728 74.3 72.4 64.9 72.59 75.3 72.7 66.1 72.7

Table 2: Accuracy rate of Fusion feature (k-NN)

The experimental results are reported in terms of correctly classified

scenes of the testing dataset. The accuracy rate of Individual features

and fusion features of Probabilistic Neural Network are as shown in

Table 3 and Table 4, respectively.

Train:Test GABOR GLCM SHAPE50 - 50 79.3 76 8060 - 40 80.8 75.8 80.8

Table 3: Accuracy rate of Individual feature (PNN)


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Train:Test GAB_GLC GAB_SHA GLC_SHA ALL_COMB

50 - 50 79.3 79.3 76.7 78.7

60 - 40 80.8 78.3 77.5 77.5

Table 4: Accuracy rate of Fusion feature (PNN)

In this paper, the proposed model fails in k-NN classifiers. Therefore,

classification technique can be extended, by considering various

classifiers fused together to get better accuracy to the proposed

methodology.

4. Conclusion

In this work, we have used texture feature and shape feature for natural

scene classification. It was found that texture and shape feature were

effective for natural scene classification. The proposed model efficiently

handles individual feature extraction and fusing the features, by

employing k-Nearest Neighbor (k-NN) and Probabilistic Neural Network

(PNN). It is observed that the individual feature yields good results when

compared with fusion feature result. Experimental results and analysis

using our dataset show that the proposed method i.e., Probabilistic

Neural Network classifier achieves 80.8% significantly better natural

scene classification performance than the k-Nearest Neighbor 80% for

individual feature and fusion feature. The study revealed that the

proposed approach performs well. It is clear that the proposed work is

simple and effective to different type’s natural scene classification and

also it performed well for different variations of light intensity for

natural scene, thus it suits to classification technique effectively. This

method showed high rate of accuracy for PNN classifier i.e., 80.8%.


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Reference

[1] Fengcai Li, Guanghua Gu, and Chengru Wang: Scene categorization

based on integrated feature description and local weighted feature mapping, in: Computer and Electrical Engineering 38 (2012) 917-925

[2] Zhan-Li Sun, Deepu Rajan, and Liang-Tein Chia: Scene classification using multiple features in a two-stage probabilistic

classification framework, in: Neurocomputing 73 (2010) 2971-2979

[3] Deng and Jianhua Zhang: Combining Multiple Precision-Boosted

Classifiers for Indoor-Outdoor Scene Classification, in: The information science discussion paper series, may 2006, ISSN 1172-6024

[4] Jiebo Luo and Matthew Boutell: Natural scene classification using overcomplete ICA, in: Pattern Recognition 38 (2005) 1507-1519

[5] Hamamoto, Y., A Gabor Filter-based Method for Fingerprint Identification, “Intelligent Biometric Techniques in Fingerprint and Face

Recognition, eds. L.C. Jain et al”, CRC Press, NJ, pp.137-151, 1999.

[6] Resmana Lim, and M.J.T. Reinders, “Facial Landmark Detection

using a Gabor Filter Representation and a Genetic Search Algorithm”, proceeding of ASCI 2000 conference.


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Kannada Handwritten Word Recognition in Bank Cheque: A Study

Nandeesh P Asst. Professor, Department of Computer Science

JSS College for Women, Chamarajanagar [email protected]

ABSTRACT

This paper presents an automation of Kannada handwritten bank

cheque words recognition system in that main challenge is

recognition of handwritten words and signature. The

identification of Kannada handwritten words plays an important

role because in Kannada script many words have a same size and

same shape. The recognition of Kannada handwritten bank

cheque words and signature is an important application in banks

and other organizations. In this work we consider dataset

contains 50 documents for each documents contains the 119

Kannada handwritten words and 28 signatures totally consider

120 classes. Here we extracting the three different types of feature

namely Gabor, LBP (local binary pattern) and LPBV (local binary

pattern variance). The effect of each feature and their

Combination in the words and signature classification is analyzed

using the K-nearest neighbour classifiers. It is common to

combine multiple categories of features into a single feature

vector for the classification and also we apply the dimensionality

reduction technique. Calculated Classification results based on

feature extraction methods, varying the K values and randomly

splitting the testing and training samples.

Keywords Document Image Processing, Cheque analysis, Segmentation

------------------------ ---------------------------


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1. INTRODUCTION

Kannada script is the visual form of Kannada language. It originated

from southern Bramhi lipi of Ashoka period. It underwent modifications

periodically in the reign of Sathavahanas, Kadambas, Gangas,

Rastrakutas, and Hoysalas. Even before seventh-Century, the Telugu-

Kannada script was used in the inscriptions of the Kadambas of

Banavasi and the early Chalukya of Badamiin the west. From the

middle of the seventh century the archaic variety of the Telugu-

Kannada script developed a middle variety. The modern Kannada and

Telugu scripts emerged in the thirteenth Century. Kannada script is

also used to write Tulu, Konkani and Kodava languages [3]. The bank

Cheques are still widely used all over the world for financial

transactions. Handwritten bank cheques are processed manually every

day in developing countries. In such a manual verification, people are

written information including signature, legal amounts in words present

on each cheque has to be visually verified. Here we review of

identification on Kannada handwritten bank cheque words. In this work

we consider the legal amount written in the words, amount written in

the numerical, account number and signature blocks. In that main

challenge is recognition of hand written words, recognizing a person

based on their signatures and recognizing the numeral, Since

identification of hand written words plays an important role in

analyzing the Kannada handwritten words. The recognition of

handwritten legal amount in words of Kannada language is challenging

because of similar size and shape of many words. Moreover many words

have same suffixes or prefix.

2. RELATED WORK


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Jayadevan R, et al. [1] they proposed recognition technique is

combination of two approaches. The first approach is based on gradient

structural and cavity (GSC) features along with a binary vector

matching (BVM) technique. The second approach is based on vertical

projection profile (VPP) feature and dynamic time warping (DTW). A

number of highly matched words in both the approaches are considered

for the recognition step in the combined

approach based on a ranking scheme. The dataset has been grouped

into three sub-datasets namely DB1, DB2 and DB3. DB1 contains data

collected from 90 individuals in Marathi language where each individual

contributed 114 word templates and a hand written cheque. The DB1

has 10,260(114×90) handwritten words and 90 handwritten cheques in

Marathi language. DB2 also has data in Marathi language, collected

from 70 individuals with comparatively poor handwriting. DB2 has

7,980(114×70) handwritten words and 70 handwritten cheques. DB3

contains data in Hindi language collected from 80 individuals. Each

individual contributed 106 word templates and a handwritten cheque.

The DB3 has 8,480(106×80) handwritten words and 80 handwritten

cheques in Hindi language. The three sub-datasets collectively have

26,720 handwritten Devanagari words and 240 handwritten cheques.

The result is 55.2% to 80.23% dependent upon the 3 datasets.

Shreedharamurthy S K, et al. [12] they developed the Neural Network

based Kannada Numerals Recognition System in this paper a novel

approach for feature extraction in spatial domain to recognize

segmented Kannada numerals using artificial neural networks. They

develop the handwritten Kannada numeral recognition system using

spatial features and neural networks. Handwritten numerals are scan

converted to binary images and normalized. The features are extracted


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using spatial coordinates and are classified using the feed forward

neural network classifier.They used spatial features and artificial neural

network as classifier. To recognizes the hand written Kannada

numerals. They have used 100 samples of numerals from the created

data base, sample patterns. The accuracy based Out of which 80

patterns used for training phase and 20 samples for testing phase.

Mehta M, et al. [2] they develop the Automatic Cheque Processing

System (English) they consider the forgery detection. An account holder

gives cheques to another person as account payee or self-cheque. It is

been observed that a number of forgery cases have been registered as

cheque forgery, where some person has forged the signature of another

person and provided a self-cheque to himself. In this paper we propose

a mechanism for

recognition of cheque fields, like name, amount and also verify the

signature and its authenticity. We propose a unique two stage model of

Automatic Cheque processing with detecting skilled forgery in the

signature by combining two feature types namely Sum graph and HMM

and classify them with knowledge based classifier and probability

neural network. We proposed a unique technique of using HMM as

feature rather than a classifier as being widely proposed by most of the

authors in signature recognition. The accuracy based on good correct

classification rate for any number of classes. The lowest rate of correct

classification is 86% and the highest is 92%.

Dhandra B.V et al. [17] they develop Zone Based Features for

handwritten and printed Mixed Kannada Digits Recognition they

consider the field of Optical Character Recognition (OCR), zoning is

used to extract topological information from patterns. They propose


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Zone based features for recognition of the mixer of Handwritten and

Printed Kannada Digits. A digit image is divided into 64 zones and pixel

density is computed for each zone. This procedure is sequentially

repeated for entire zone. Finally 64 features are extracted for

classification and recognition. There could be some zone column/row

having empty foreground pixels. Hence the feature value of such

particular zone column/row in the feature vector is zero. The KNN

classifiers are used to classify the mixed handwritten and printed

Kannada digits. They have obtained 97.32% & 98.30% recognition rate

for mixed handwritten and printed Kannada digits by using KNN

classifiers respectively.

Dinesh Acharya U. [11] they proposed the Multilevel Classifiers in

Recognition of Handwritten Kannada Numerals The recognition of

handwritten numeral is an important area of research for its

applications in post office, banks and other organizations. In that paper

presents automatic recognition of handwritten Kannada numerals

based on structural features. Five different types of features, namely,

profile based 10-segment string, water reservoir; vertical and horizontal

strokes, end points and average boundary length from the minimal

bounding box are used in the recognition

of numeral. The effect of each feature and their combination in the

numeral classification is analyzed using nearest neighbor classifiers. It

is common to combine multiple categories of features into a single

feature vector for the classification. Instead, separate classifiers can be

used to classify based on each visual feature individually and the final

classification can be obtained based on the combination of separate

base classification results.


August 2013

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Dhandra B.V. et al. [18] they develop a script independent approach for

handwritten bilingual Kannada and telugu digits recognition,

handwritten Kannada and Telugu digits recognition system is proposed

based on zone features. The digit image is divided into 64 zones. For

each zone, pixel density is computed. Feature extraction is a problem of

extracting the relevant information from the preprocessed data for

classification of underlying objects/characters. The preprocessed digit

image is used as an input for feature extraction. For extracting the

potential feature from the handwritten digit image, the frame containing

the preprocessed/normalized image is divided into non overlapping

zones of size 8 x 8 and obtained 64 zones. For each zone, the pixel

density is computed and there pixel densities are used as a feature for

recognition. Hence, 64 features vector is used for recognition of a digit.

The KNN and classifiers are employed to classify the Kannada and

Telugu handwritten digits independently and achieved average

recognition accuracy of 95.50%, 96.22% and 99.83%, 99.80%

respectively. For bilingual digit recognition the KNN classifiers are used

and achieved average recognition accuracy of 96.18%, 97.81%

respectively.

José Eduardo Bastos dos Santos. [7] they develop the Text Extraction in

Bank Cheque Images features are used to shape features, Mean,

Standard deviation, Skewness, Range, Solidity, Extend, and Area

feature, the subtraction approach means by subtracting empty cheque

from filled cheque. In subtraction approach the extracting information

is losing. It means geometrical distortions, alignment of cheque,

graphical security elements, accuracy is based on data sample the

result is 93%.


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3. APPLICATIONS

The applications can be found in the following areas,

Banking system : This system is used for money transactions and

withdrawn the amount

and surety.

Insurance companies: It used to pay the premiums, width draw

the annual amount.

Gold loan companies: In Gold loan companies are used to

repaying the loans.

Government office: In Government office issuing the salary to the

employees.

4. CHALLENGES

Segmentation of cheque

Identification of handwritten words

Identification of word denomination

Identification of numeral denomination

Comparison of word and numeral denomination

Person identification based on signature

Writer identification

5. MOTIVATION

Manual cheque processing is more time consuming

Processing handwritten text is challenging specially regional

languages

Only few works are reported in the literature on handwritten

word recognition in kannada

6. OBJECTIVES


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Collection of dataset.

Pre-processing.

Identification of word and numeral denomination.

Study of suitable classifier models will be carried out in this work.

7. CONCLUSION

In this paper we worked recognitions of Kannada handwritten bank

cheque words. The recognition of Kannada handwritten words is very

difficult task because handwriting is different for different person.

8. REFERENCE [1] Jayadevan R., Kolhe S.R., Patil P.M. and Pal U., 2011. Database

development and recognition of hand written Devanagari legal amount words (Hindi). International Conference on Document Analysis and Recognition.

[2] Mehta M., Sanchati R. and Marchya A., 2010. Automatic Cheque

Processing System.

[3] Patil P.B. and Ramakrishna A.G., 2008. Word level multi-script

identification. Pattern recognition letters, Vol.29, pp 1218 – 1229. [4] Guru D.S, Ravikumar M, and Harish B.S 2011.A Review on Offline

Handwritten Script Identification International Journal of Computer Applications (0975 – 8878) on National Conference on Advanced

Computing and Communications. [5] Madasu V.K., Yusof M.H.M., Hanmandlu M and Kubik K 2010.

Automatic Extraction of Signatures from Bank Cheques and other Documents Intelligent Real-Time Imaging and Sensing group, School of Information Technology and Electrical Engineering,

University of Queensland, QLD 4072, Australia.

[6] Jayadevan R., Kolhe S.R., Patil P.M. and Pal U., 2011. Automatic processing of handwritten bank cheque images a survey.

[7] Santos J.E.B.D Text Extraction in Bank Cheque Images: A Prospective View 2010.


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[8] Mingqiang Y, Kidiyo K, and Joseph R A survey of shape feature extraction techniques (2008). Author manuscript, published in

Pattern Recognition, Peng-Yeng Yin (Ed.) 2008 43-90.

[9] Zhang D and Guojun Lu Review of shape representation and description techniques (2003). Pattern Recognition 37 2004.

[10] U. Pal, N. Sharma, F. Kimura, "Recognition of Handwritten Kannada Numerals", 9th International Conference on Information Technology

(ICIT'06), pp. 133-136, 2006.

[11] Dhandra B.V, Mukarambi G and Hangarge M Kannada and English

Numeral Recognition System 2011 International Journal of Computer Applications (0975 – 8887) Volume 26– No.9, July 2011.

[12] Shreedharamurthy S K and Sudarshana Reddy H.R. Neural

Network based Kannada Numerals Recognition System 2012. International Journal of Computer Applications (0975 – 8878) on National Conference on Advanced Computing and Communications

- NCACC, April 2012.

[13] Kruizinga P,Petkov N and Grigorescu S.E Comparison of texture features based on Gabor filters 1999. Proceedings of the 10th International Conference on Image Analysis and Processing, Venice,

Italy, September 27-29, 1999, pp.142-147. [14] Amayeh G, Tavakkoli A and Bebis G Accurate and Efficient

Computation of Gabor Features in Real-Time Applications 2008.

[15] Liao S, Zhu X, Lei Z, Zhang L, and Stan Z. Li Learning Multi-scale

Block Local Binary Patterns for Face Recognition 2011.Heikkil M, Ainen M.P and Schmid C.B Description of Interest Regions with Local Binary Patterns (2011). INRIA Grenoble, 655 Avenue de

l’Europe, 38330 Montbonnot, France.

[16] Ilonen J, Kamarainen J K, K¨alvi¨ainenEfficient H computation of Gabor features 2005. Department of Information Technology. Lappeenranta University of Technology, P.O.Box 20, FIN-53851

Lappeenranta, Finland.


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[17] Mallikarjun hangare and B V Dhandra., 2010. Offline handwritten script identification in document images. International journal of

computer applications. Vol 4, pp 6 – 10

[18] B. V. Dhandra, Mallikarjun Hangarge, Gururaj Mukarambi,Spatial Features for Handwritten Kannada and English Character Recognition Special Issue on RTIPPR-10, International Journal of

Computer Applications, pp.146-150, Aug-2010.


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A Review on Automation of Ayurvedic Plant Recognition

Pradeep Kumar N PG Department of Computer Science,

JSS College of Arts, Commerce and Science, Ooty Road, Mysore-25

Abstract

Plant classification is a more challenging task when compared to

classification of other categories such as face, objects etc The tribal

people in India classify plants according to their medicinal values. In

the system of medicine called Ayurveda, identification of medicinal

plants is considered an important activity in the preparation of herbal

medicines. We have obtained around 3000 images of medicinal plants,

in that the plants are classified into two types namely one is normal

regular medicinal plants another one is Bonsai medicinal Plants. In

Both the type around 70 Classes of Plant species each Classes with an

around 25 images are created. The images of medicinal plants are of

different pose, with cluttered background under various lighting

conditions and climatic conditions are used.

Keywords: Ayurveda, computer vision, database, Challenges

------------------------ ---------------------------


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1. Introduction:

The tribal people in India classify plants according to their

medicinal values. In the system of medicine called Ayurveda,

identification of medicinal plants is considered an important activity in

the preparation of herbal medicines.

Ayurveda, the science of life, prevention and longevity is the

oldest and most holistic medical system available on the plant today.

From over 5000 years ago in India, it was said to be a world medicine

dealing with both body and the spirit. Ayurvedic medicine, in the United

States, is an “alternative” medical practice that claims it is based on the

traditional medicine of India.

Ayurveda is derived from the two Sanskrit terms: ayu meaning life

and veda means Knowledge or science. Ayurveda, “The knowledge for

long life”. Ayurveda has long been the main system of health care in

India, although conventional (Western) medicine is becoming more

widespread there, especially in urban areas. About 70 percent of India’s

population lives in rural areas; about two-thirds of rural people still use

Ayurvedic medicinal plants to meet their primary health care needs.

Medicinal plants form the backbone of a system of medicine

called Ayurveda and is useful in the treatment of certain chronic

diseases. Ayurveda is considered a form of alternative to allopathic

medicine in the world. This Indian system of medicine has rich history.

Ancient epigraphic literature speaks of its strength. Ayurveda certainly

brings substantial revenue to India by foreign exchange through export

of Ayurvedic medicines, because of many countries inclining towards

this system of medicine. Considerable depletion in the population of


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certain species of medicinal plants, we need to grow these plants in

India.

It is necessary to make people realize the importance of medicinal

plants before their extinction. It is important for Ayurveda practitioners

and also traditional botanists to know how to identify and classify the

medicinal plants through computers.

As the project is related to development of design methodologies

of Image Processing, Pattern Recognition and also Knowledge

about the Taxonomy of Ayurvedic Plants.

The proposal is an interdisciplinary where two Different fields viz.,

Computer Science and AYURVEDA are brought together for the

purpose of preserving information about Ayurveda Plants for

Future Generation.

Medicinal plants are classified based on internal and external

features. The external features of plants are helpful in their

identification. According to the plants taxonomy, we find classification

of plants based on the shapes of their leaves and flowers. But plant

classification is based on color histogram, edge direction, edge

histogram is not being attempted by human beings.

2. Related work

S M Patil proposed content based image retrieval system using

color texture and shape features. They use 700 images to train the

system. They used the color histogram to represent the color

distribution of the image. They use HSV color model. They use recall

and precision measures for calculate the performance measures.


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Dubey R S et al., proposed a multi feature content based image retrieval

system. They use color histogram to representation of distribution of

colors in an image. They use RGB and HSV color space models. They

use the first order moment mean, second order moment standard

deviation, third order moment skewness.

Here they use color histogram, color moments, texture and edge

histogram. To extract the edge features in the image block is to apply

the digital filters in spatial domain. Divide the image into 4 sub block

and assigning the average gray levels for that sub block and represents

the filter coefficients for vertical, horizontal, 45 degree diagonal and 135

degree diagonal. They use Euclidean distance measure as a similarity

measure. They combine all

four features in this work. They use 11 images for this work and they

use 1 image as a testing data and others are training images. They got

78% of accuracy for the color histogram with the 10th image.

Singh S. M. and Hemechandra K. worked on the content based image

retrieval using color moments and Gabor texture feature. In this they

use low level color features and low level texture features. For extraction

of color feature they convert RGB image to HSV color space. Divide the

images into three equally horizontal non overlapping regions. Extract

the moments from those regions. Then calculate the distance between

query image and image in database. They use the Canberra distance

measure for calculate the distance. They got the accuracy of 43.6% with

the color moments with the whole image and the accuracy of 59.0%

with color moments with dividing the image into three equal non

overlapping regions.


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Bhuravarjula H.H.P.K and Kumar V.N.S.V proposed a novel content

based image retrieval using variance color moment. In this work the

creation and digitization of images and image retrieval have become

easier, huge image databases have become more popular. The area of

retrieve images based on the visual content of the query picture

intensified recently, which demands on the quite wide methodology

spectrum on the area of image processing. Content Based Image

Retrieval (CBIR) has therefore evolved into necessity. Due to the

increased garbage value it is very important to design a CBIR system to

retrieve images from the database in a very efficient manner. In this

paper we are going to propose a color image retrieval method based on

the primitives of color moments. At the starting stage the image is

divided into four segments. Then the color moments of all segments are

extracted and clustered into four classes. At the next stage we will

consider the mean moments of each class as a primitive of the image.

All the primitives are used as features and each class mean is merged

into a single class mean. The distance between the input query image

mean with the corresponding database images are calculated by using

SAD method. The analysis results proved that the CBIR using our new

method has the better performance than the existing method.

Husin et al.,(2012) introduced Embedded portable device for herb leaves

recognition using image processing techniques and neutral network

algorithm. This paper has presented a device capable of recognize herbs

species recognition and classification based on leaves structural

characteristics. A novel individual leaf extraction computer program

was developed based on grayscale, canny edge detector and back

propagation neural network algorithm. With the use of computer and

embedded system automated classification of herbs leaves plant

becomes more convenient, and efficient. By using BPNN, the rapid


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recognition for twenty kinds of herbs species leaves was realized and

the average correct recognition rate reached 98.9% . The training set

contains minimum 30 species for each type of leaf in each data file. The

larger the number of species used in training set, the higher the

number of output nodes thus enhances the recognition ability.

Guru D.S., et al investigate the effect of texture features for the

classification of flower images. A flower image is segmented by

eliminating the background using a threshold basedmethod. The

texture features, namely the color texture moments, gray-level co-

occurrence matrix, and Gabor responses, are extracted, and

combinations of these three are considered in the classification of

flowers. In this work, a probabilistic neural network is used as a

classifier. To corroborate the efficacy of the proposed method, an

experiment was conducted on our own data set of 35 classes of flowers,

each with 50 samples. The data set has different flower species with

similar appearance (small inter-class variations) across different classes

and varying appearance (large intra-class variations) within a class.

Also, the images of flowers are of different pose, with cluttered

background under various lighting conditions and climatic conditions.

The experiment was conducted for various sizes of the datasets, to

study the effect of classification accuracy, and the results show that the

combination of multiple features vastly improves the performance, from

35% for the best single feature to 79% for the combination of all

features. A qualitative comparative analysis of the proposed method

with other well-known existing state of the art flower

classification methods is also given in this paper to highlight the

superiority of the proposed method.


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Yu et al., introduced a color texture moments for content-based image

retriveval(CBIR). They have proposed a novel low-level feature CTM for

content-based image retrieval systems. They adopt LFT as a texture

representation scheme and derive eight characteristic maps for

describing different aspects of co-occurrence relations of image pixels.

Then they calculate the first and second moments of these maps as a

representation for the distribution of natural color image pixels. They

operate the LFT in the color space since it not only corresponds to

visual perception but also overcomes some shortcomings of the HSV

color space. Experiments on an image library containing 10,000 Corel

images and 200 queries demonstrate the effectiveness of the new

method.

Singh and Hemechandra worked on the content based image retrieval

using color moments and Gabor texture feature. In this they use low

level color features and low level texture features. To extracting the low

level texture features they apply Gabor filters on the image with 4 scale

and 6 orientations. They obtained an array of magnitudes. Mean,

standard deviation of magnitudes are used to create texture feature.

Canberra distance measure is used for computing the distance. For this

work they use WANG database that containing 1000 corral images are

in the JPEG format. In this there are 10 category and 100 images with

each category. They combine both feature of color and texture. For

measuring the performance they use precision and recall measures.

They got the accuracy of 43.6% with the Gabor texture feature and

considering the whole image as well as the dividing of image into three

equal non overlapping regions. They got the accuracy of 61.0% with

combining the Gabor texture feature and color moment feature along

with the image is divided into three equal non overlapping regions.


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Wu et al., worked on a leaf recognition algorithm for plant classification

using probabilistic neural network. In this paper, they employ

Probabilistic Neural Network (PNN) with image and data processing

techniques to

implement a general purpose automated leaf recognition for plant

classification. 12 leaf features are extracted and orthogonalized into 5

principal variables which consist the input vector of the PNN. The PNN

is trained by 1800 leaves to classify 32 kinds of plants with an accuracy

greater than 90%. Compared with other approaches, our algorithm is

an accurate artificial intelligence approach which is fast in execution

and easy in implementation.

In the preprocessing technique they convert RGB image into gray scale

image and then converted into binary image. Then for enhancing the

boundary of the image they apply smoothing and then filtered using the

Laplacian filter of 3*3 spatial masks.

In this work they extract five basic geometric feature. They are,

Diameter(the longest distance between any two points on the leaf

margin), Physiological length(the distance between the two terminals

(main vein of leaf)), Physiological width(the longest distance between the

points on intersection pairs),Leaf area(the number of pixels of binary

value one on the smoothed leaf image) and Leaf perimeter(the number

of pixels consisting of leaf margin).

Also Based on these 5 basic features, they can define 12 digital

morphological features used for leaf recognition.They are smooth factor,

aspect ratio, factor form, rectangularity, narrow factor, perimeter,

perimeter ratio of physiological length and width and five vein features.


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They extract the 12 digital morphological shape features are derived

from 5 basic geometric features. To reduce the dimension of the input

vector they use principle component analysis (PCA). The probabilistic

neural network is used as a classifier. It simulates the thinking process

of human brain and it has the fast training speed. Hence they got the

90% of accuracy.

Arribas et al., introduced leaf classification in sunflower crops by

computer vision and neural networks.In this article, they present an

automatic leaves image classification system for sunflower crops using

neural networks, which could be used in selective herbicide

applications. The system is comprised of four main stages. First, a

segmentation based on rgb color space is performed. Second, many

different features are detected and then extracted from the segmented

image. Third, the most discriminable set of features are selected.

Finally, the Generalized Softmax Perceptron (GSP) neural network

architecture is used in conjunction with the recently proposed

Posterior Probability Model Selection (PPMS) algorithm for complexity

selection in order to select the leaves in an image and then classify

them either as sunflower or non-sunflower. The experimental results

show that the proposed system achieves a high level of accuracy with

only five selected discriminative features obtaining an average Correct

Classification Rate of 85% and an area under the receiver operation

curve over 90%, for the test set. From the literature survey it is

understood that color, texture, shape features play a major role in plant

classification.

They propose 13 morphological features : Perimeter, Centroid, Area ,

Major axis of the best fit ellipse, Minor axis of the best fit ellipse, Height,


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Width, Area to length ratio, Compactness, Ratio of area to the perimeter

squared, Elongation, Logarithm height/width ratio, Perimeter to

broadness (PTB), Length to perimeter ratio.

In this literature survey we clearly known there is huge research are

done related to Plant Classification based on the Flower and Leaf part.

The research related to Plant Classification based on the entire Plant

Portions is very less and they get Low Accuracy rate compare to other.

Hence I motivated and done a small attempt.

The most challenging task in recognition of ayurveda plats are as

follows

1. Segmentation of plants from the background

2. Selection of suitable features for identification of plants

3. Study of suitable classifiers

4. Data set also pose a challenge as there is no standard dataset for this

purpose.

Hence in this work our main interest is to study the effect of

shape, color and texture feature in plant classification with the following

objectives:

Creation of large DATABASE of Medicinal Plants.

The collecting of images is the main task in this work. For this we

collect more than 3000 images. The image acquisition or collection is a

challenging task. Because of uncontrolled environment and climatic

condition the lighting and brightness are changes with respect to the

seasonal change.

3. Ayurvedic Plant Database

Creation of ayurvedic database involved two stage, names


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1. Image Acquisition.

2. Plant Segmentation.

3.1. Image Acquisition

We have obtained around 3000 images of medicinal plants using the

color Digital Camera having resolution of 8 Mega pixels. The views in all

the 8 directions, namely right, left, top, bottom, and other diagonals are

obtained. The images of plants are captured in such a way that the

plant trunk and leafy portion are more significant with clear difference

in color and edge pattern.

3.2. Plant Segmentation

Before features are extracted from the plant image , the plant has to be

segmented. The images of medicinal plants are filtered by Manually to

remove any noise introduced at the time of acquisition of images. The

goal is to automatically segment out the plant given only that the image

is known to contain a plant, but not other information on the class or

pose. Plants in images are often surrounded by greenery in the

background.

33. Dataset Information

The images of different medicinal plants are considered in this

work. We have collected plants like Tulasi(Ocimum Sanctum), Dodda

Patre(Cuban

Oregano), Aloe Vera(Laval sara), Ekki(Gigantea), Tumbe(Dronapuspi),

Vajravalli etc.. These plants are very useful in daily life has medicine.

3.2.1. Dataset Collection

In this project work we have created our own database. In order

to create the database we have took photographs of plants that are

found in and around of Mysore city, Karnataka, India.

Table.1 Dataset Collection Information


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3.2.2. Regular medicinal plants database

Table.2 Regular Medicinal Plants Dataset List

REGULAR MEDICINAL PLANTS DATABASE

Sl . No. Scientific Name Kannada Name

1 Acacia Catechu Kaggali

2 Adhatoda Vasica Aadusoge

3 Alangium Lamarckii Ankole Mara

4 Aloe Vera Loka Sara

5 Alpinia Officinarum Sannaraashme

6 Annona squamosa Seethaphala

7 Areca Catechu Adake

8 Artabotrys odouratissimus Manoranjini

9 Asparagus Adscendens Safeidh Musalee

10 Asparagus Racemosus Aashaadhi beru

11 Averroha Carambola Kamaraakshi

12 Azadirachta Indica (Neemb) Bevu

13 Bauhinia Tomentosa Kanchuvala

14 Calatropis Procera Yakka

15 Calophyllum Inophyllum SuraHonne

16 Cassia Auriculata Aavarike

17 Cassia fistula Kakke

TYPE NO. OF CLASSES NO. OF IMAGES

PER CLASS

Regular Ayurvedic Plants

72 [ 20-25 ]

Bonsai Plants 67 [ 20-25 ]


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18 Citrus Medica Maadhala

19 Clerodendron Infortunatum Vishaprahari

20 Clitorea Terneatea Girikarnike

21 Coleus Spicatus Dhoddapathri

22 Dalbergia Latifolia Thodagatta or Beete

23 Datura Alba Bili Ummatti

24 Eclipta Alba Garagadha Soppu

25 Emblica Officinalis Nelli

26 Eriodendron Anfractuosum BiliBhooruga

27 Eugenia Jambolana Jamnerele

28 Ficus Religiosa Arali

29 Gardenia Gummifera Dikkaamali

30 Gauzuma Tomentosa Bhadhraaksha

31 Gmelina Arborea Shivani

32 Grewia Asiatica Dhadasala hannu Phaalasaa

33 Hibiscus Rosa Sinensis Dhasavaala

34 Ixora Coccinea Kepala

35 Lawsonia Alba Gowrantee

36 Moringa oleiera Nugge

37 Murraya Koenigii Karibevu

38 Musa Sapientum Baale

39 Myrtus caryophyllata Lavanga

40 Ocimum Basilicum Tulasi

41 Psidium Guajava Sheebe

42 Pterocarpus Santalinus RakthaChandhana

43 Pterospermum Acerifolium Kanakachampa

44 Punica Granatum Dhalimbe

45 Putranjiva Roxburghii Puthrajivi

46 Santalum Album Shri Gandha

47 Sapindus Trifoliatus Antuvaala

48 Saraca Ashoka Ashoka

49 Solanum Indicum Heggulla

50 Solanum Trilobatum Habbu Sonde gida

51 stereosppermum suavelens Phaadhari

52 Streblus Asper Mettlimara

53 Tabebuia Rosea NA

54 Tamarindus Indica Hunuse


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55 Terminalia Tomentosa Karimatti

56 Thevetia Nerifolia Haladhi Kanigale

57 withania Somnifera Hiremaddhu

58 NA Amrutha Balli

59 NA Ala

60 NA Bhraamhi

61 NA Thengu

62 NA Dharbe

63 NA Gasagase

64 NA Halasu

65 Zizyphus Jujuba Elachi

66 NA Maavu

67 NA Nimbe

68 NA Papaya(parangi)

69 NA Shunti

70 NA Thumbe

71 NA Vajravalli

72 NA Veelya

3.2.3. Bonsai plants database

Table.3 Bonsai Plant Dataset List

BONSAI PLANTS DATABASE

Sl .

No. Scientific Name Kannada Name



3 Akasha Mallige NA

4 Almonda Hindhola

5 Aloe Barbadensis Kumaari

6 Angle Marmelos Chitha(Billwa)

7 Aralia Cordata NA

8 Artocarpus Heterophyllus Halasu

9 Australian Ficus NA

10 Australian Natalensis Brundhavana Saaranga

11 Borassus Flabellifer Toddy Palm

12 Bougainvillea NA


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13 Brazilan Rain Tree NA

14 Candle Tree NA

15 Casuarina Equisetifolia NA

16 Cicca Acida NA

17 deva parijatha deva manohari

18 Divi Divi Kalyani

19 Eragrostis Cynosuroides Darbe

20 Ficus Apolo NA

21 Ficus Bengalensis NA

22 Ficus Benjamina NA

23 Ficus Blackenea NA

24 Ficus Bodhi NA

25 Ficus Curlie NA

26 Ficus Glomerata (Vrushabha) NA

27 Ficus Hispida Vajra kanthi

28 Ficus Infectoria NA

29 Ficus Infectoria Basari(Uthara)

30 Ficus Jaquinia NA

31 Ficus Jaquinia on rock NA

32 Ficus Jaqvinia NA

33 Ficus Lipstic NA

34 Ficus Lipstick (Cascade) NA

35 Ficus Long Island Hari Narayani

36 Ficus Microcarpa NA

37 Ficus Mysorensis NA

38 Ficus Natalensis NA

39 Ficus Nuda NA

40 Ficus Pilkhan NA

41 Ficus Religiosa NA

42 Ficus Retusa Rasika ranjini

43 Ficus Retusa Vajra kanthi

44 Ficus Specis NA

45 Ficus Tallboti NA

46 Grevillea Robusts Ralia NA

47 Jaquina Ruscifolia NA

48 Juniperus (semi Cascade) NA

49 Kannada gowla NA


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50 kirni punnagavaraali

51 Christmas Tree Malaya Maarutha

52 Mangifera Indica NA

53 Mangifera Indica Maavu

54 Mimsops Elengi Ranjalu

55 Phyllanthus distichus lavali

56 Phyllanthus Emblica Betta nallikayi

57 Podocarpus Polystachyus NA

58 Punarvasu Bambo (Bhidhiru)

59 Sand Paper Ficus NA

60 Sapota (Variegated) NA

61 Saraca Indica Ashoka

62 Sceffera Arboricola Cultivar NA

63 Schefflera NA

64 Sonefflera arboricola NA

65 Spondias Mangifera Aamate(Hastha)

66 Tamarindus Indica NA

67 Vitis Quadrangularis Asthi Shrunkala

Conclusion

In this work we have created a database of two different classes.

The images are captures and segmented. The database is made

publically available.

REFERENCES

Anami B.S., Nandyal S.S. and Govardhan A., 2010. A combined color, texture and edge features based approach for identification

and classification of Indian medicinal plants, International Journal of Computer Applications, Vol.6, No.12, pp. 0975-8887.

Wu S.G., Bao F.S., Xu E.Y., Wang Y.X. and Xiang Q.L., 2007. A

Leaf Recognition Algorithm for Plant Classification Using

Probabilistic Neural Network.

Yu H., Li M., Zhang H. J. and Feng J. Color Texture Moments For Content-Based Image Retrieval.


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59

Husin Z., Shakaff A.Y.M., Aziz A.H.A., Farook R.S.M., Jaafar

M.N., Hashim U. and Harun A., 2012, Embedded portable device for herb leaves recognition using image processing techniques

and neural network algorithm, Computers and Electronics in Agriculture Vol.89, pp.18–29.

Arribas J. I., Sanchez-Ferrero G. V., Ruiz-Ruiz G. and Gomez-Gil J., 2011, Computer and Electronics in Agriculture Vol.78, pp.9-

18.

Patil S.M., International Journal of Computer Science &

Engineering Technology.Dubey R.S., Choubey R. and Bhattacharjee J., 2010, International Journal on Computer Science and Engineering Vol.o2, No.06, 2149.

Singh S.M. and Hemachandran 2012, International Journal of

Computer Science Issues, Vol. 9, Issue 5, No.1, September.

Bhuravarjula H.H.P.K. and Kumar V.N.S.V., 2012, A novel

content based image retrieval using variance color moment. International Journal of Computer and Electronics Research

Vol.1, Issue.3.

Guru D.S., Kumar Y.H.S., and Manjunath S., Texture features in

flower classification. Mathematical and Computer Modelling 54 (2011), pp.1030–1036.

Guru D.S., Mallikarjuna.P.B., Manjunath S and Shenoi M.M, 2012 Intelligent Automation and Soft Computing, Vol. 18, No. 5,

pp. 577-586.


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. A Review on Neurological Disorders

Mr. Maheswara Prasad1, Dr. Manjunatha Rao L2 Research Scholar, CMJ University, Meghalaya, India

Abstract:

Major Neurodegenerative diseases are like defining the continent of

Europe: part history, part science, part politics, and to cap it, both

could have an effect on health and prosperity. A big advantage of the

term is that it is a concept that patients can relate to from parallels in

everyday life. Wearing out in time of certain components sometimes

replaceable, sometimes not encompasses principles of selective

neuronal death as a primary event with age as a major risk factor and

good remedies patchy.

Though the causes may differ, patients with neurodegenerative

disorders are likely to show localized to generalized atrophy of the brain

cells leading to compromise in both mental and physical functions.

Mentally, the patients will exhibit forgetfulness, poor memory, decrease

in mental capacity, emotional disturbance, poor speech, etc. Physically,

the patients will exhibit partial to complete incontinence, aspiration of

food particles, tremor, poor balance, muscle rigidity, muscle paralysis,

etc. These decreases in mental and physical functions dramatically

reduce the quality of life for the patients and increase the burden on the

family and care-takers.

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1.0 Introduction:

Defining neurodegenerative diseases is like defining the continent of

Europe: part history, part science, part politics, and to cap it, both

could have an effect on health and prosperity. A big advantage of the

term is that it is a concept that patients can relate to from parallels in

everyday life. Wearing out in time of certain components sometimes

replaceable, sometimes not encompasses principles of selective

neuronal death as a primary event with age as a major risk factor and

good remedies patchy.

Though the causes may differ, patients with neurodegenerative

disorders are likely to show localized to generalized atrophy of the brain

cells leading to compromise in both mental and physical functions.

Mentally, the patients will exhibit forgetfulness, poor memory, decrease

in mental capacity, emotional disturbance, poor speech, etc. Physically,

the patients will exhibit partial to complete incontinence, aspiration of

food particles, tremor, poor balance, muscle rigidity, muscle paralysis,

etc. These decrease in mental and physical functions dramatically

reduce the quality of life for the patients and increase the burden on the

family and care-takers.

1.1. Trace elements.

There are three groups into which the elemental constituents of a

biological material can be placed. These are the major, minor and the

trace elements. The major and minor elements make up 99% of the

total constituents of biological matter while the remaining 1% are

known as the trace elements. The trace elements act primarily as

catalysts in enzyme systems of cells where they serve a wide range of

functions[Val71].


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1.1.1. Essential and non-essential trace elements.

There are approximately twenty six out of the ninety naturally occurring

elements which are known to be essential for animal and human life.

[Und77]. The majority of trace elements essential to life lie between the

atomic numbers 23 through 34. Many definitions have been formulated

to determine whether elements are essential or not such as Mertz who

stated that, "An element is to be considered to be essential if its

deficiency consistently results in impairment of a function from optimal

to sub-optimal" [Mer70]. Cotzias [Cot67] also gave a comprehensive

definition which lists six sets of criteria to determine whether or not a

trace element is essential.

1.1.2. Toxic trace elements.

As well as the essential and non-essential trace elements there is a

further group of ‘toxic' trace elements. The definition of a toxic element

is complicated as elements which are essential to everyday life can also

be deemed toxic when they are either too high or too low in

concentration. This can change elements such as iron, iodine and

copper from being essential under normal conditions, into toxic

elements and categorise them with lead, cadmium and mercury which

have potentially toxic properties at even the lowest concentrations.

These practices have a disastrous effect on the long-term welfare of

human and animal populations. An example of essential elements that

are known to have a toxic/detrimental effect at high or low

concentrations are, iron, iodine and copper. Iron deficiency causes

anaemia and iodine is associated with goitre [Hey84]. But it must

always be remembered that ‘safe' dietary levels of these potentially toxic

trace elements also exist. The required concentration of an element for

‘normal' function can also vary depending on the extent to which other


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elements that affect their absorption and retention are present. These

considerations apply to all the trace elements to varying degrees but

some have more affect than others.

1.2 PARKINSON’S DISEASE

1.2.1 Introduction : Parkinson's disease may be one of the most baffling

and complex of the neurological disorders. Its cause remains a mystery

but research in this area is active, with new and intriguing findings

constantly being reported.

Parkinson's disease belongs to a group of conditions called motor

system disorders. The four primary symptoms are tremor or trembling

in hands, arms, legs, jaw, and face; rigidity or stiffness of the limbs and

trunk; bradykinesia or slowness of movement; and postural instability

or impaired balance and coordination. As these symptoms become more

pronounced, patients may have difficulty walking, talking, or

completing other simple tasks.

1.2.2. Causes : Parkinson's disease occurs when certain nerve cells, or

neurons, in an area of the brain known as the substantia nigra die or

become impaired. Normally, these neurons produce an important brain

chemical known as dopamine. Dopamine is a chemical messenger

responsible for transmitting signals between the substantia nigra and

the next "relay station" of the brain, the corpus striatum, to produce

smooth, purposeful muscle activity. Loss of dopamine causes the nerve

cells of the striatum to fire out of control, leaving patients unable to

direct or control their movements in a normal manner.

1.2.4. Treatment: Even for an experienced neurologist, making an

accurate diagnosis in the early stages of Parkinson's disease can be


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difficult. There are, as yet, no sophisticated blood or laboratory tests

available to diagnose the disease. The physician may need to observe

the patient for some time until it is apparent that the tremor is

consistently present and is joined by one or more of the other classic

symptoms. Since other forms of parkinsonism have similar features but

require different treatments, making a precise diagnosis as soon as

possible is essential for starting a patient on proper medication.

We believe that computational models will play in increasingly

important role in understanding the pathophysiology of movement

disorders such as Parkinson’s disease. And a number of groups are

beginning to apply methodologies used in understanding central pattern

generators and neuronal oscillations to the study of Parkinson’s tremor.

These studies mayyield insights that will eventually lead to better

treatments for these disorders.

The National Institute of Neurological Disorders and Stroke of The

National Institutes of Health. NIH Publication No. 94-139. Parkinson's

Disease: Hope Through Research. September 1994. Last revised

September 15, 1999. (Online)

http://www.ninds.nih.gov/patients/disorder/parkinso/pdhtr.htm"

1.3. Alzheimer's Disease:

1.3.1. Introduction: Alzheimer’s disease (AD) is a degenerative brain

disorder that affects 3-4 million Americans and accounts for over 70%

of all cases of dementia (Katzman, 1986). The disease is slowly

progressive; rates of progression from initial symptoms to end stage

dementia range from 2 to 25 years, with most patients in the 8 to 12

year range.

http://www.ninds.nih.gov/patients/disorder/parkinso/pdhtr.htm#_blank


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AD begins slowly. At first, the only symptom may be mild forgetfulness.

People with AD may have trouble remembering recent events, activities,

or the names of familiar people or things. Simple math problems may

become hard for these people to solve. Such difficulties may be a

bother, but usually they are not serious enough to cause alarm. AD

may have a very long preclinical course of 20 or more years with

biochemical and pathological changes preceding clinical symptoms.

Scientists at research centers across the country are trying to learn

what causes AD and how to prevent it. They also are studying how

memory loss happens. They are looking for better ways to diagnose and

treat AD, to improve the abilities of people with the disease, and to

support caregivers. Currently, no treatment can stop AD. However, for

some people in the early and middle stages of the disease, medications

including tacrine, donepezil, and velnacrine may alleviate some

cognitive symptoms.

1.4. Primary Health Care Models

People with learning disabilities are susceptible to many physical

illnesses, affecting virtually all organs and bodily systems. The

prevalence of such disorders is greater than that in the general

population and can have a considerable impact on the life of a person

with learning disabilities. Significant emotional and/or behavioural

disturbance and loss of adaptive skills may result. Subsequently

significant physical morbidity may remain inadequately treated.

Roy and Martin (1998) review the models of primary health care which

have been used so far. They are :

1.4.1. General Practitioner lead approach

Howells (1986 ) offered health checks to people with learning disabilities


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attending a training centre and found a significant number of

unmanaged physical disorders.

Kerr (1996) did a comprehensive health check for 28 people with

learning disabilities and compared them to matched controls in a

practice based study. The outcome was that the study group received

less of the regular screening i.e. immunisation and cervical cytology but

had more outpatient appointments and saw more specialists.

1.4.2. Specialist led approach

In this model, the specialist, usually the Consultant Psychiatrist, took

the lead in health checks. The study by Wilson and Haire (1990) in

Nottingham amongst people with learning disabilities attending an

adult training centre is an example of this model. Beange (1995) carried

out a community based study in the Northshore district of Sydney,

Australia .As in other studies, the higher incidence of unmanaged

health problems was demonstrated in both .

1.4.3. Collaborative Models

In this model, there is collaboration between the primary health care

team and the specialist services to provide comprehensive health

checks. In the first of these, a facilitator co-ordinated people with

learning disabilities having a health check at their own general

practitioner’s surgery (Martin et al, 1997). Bollard ( 1998) discusses a

model where the Community Learning Disability Nurse’s role was

extended to work with the primary health care team. Health checks

offered at GP practices were performed mostly by practice nurses.

Cassidy et al ( 1998) describe joint clinics for people with learning

disabilities where physical and psychiatric health checks are offered

during a single visit to the surgery.

In order to reduce the undetected health problems which people with


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learning disabilities have, there needs to be further training for medical

students and general practitioners. Plant ( 1997 ) demonstrated by a

confidential postal questionnaire that general practitioners often lacked

confidence in caring for people with learning disabilities. There is some

confusion about what constitutes a learning disability, degrees of

learning disability, the health needs that people with learning

disabilities have and configuration of specialist services though the

general practitioners are often in a position of knowing a great deal

about these individuals’ social situations ( Whitfield et al, 1996 ;

Marshall et al, 1995 ).

(J Geriatr Psychiatry Neurol 2002; 15:38–43).

1.7. Summary:

The success of applying computational methods to understanding

neurological disease will depend upon a number of factors. Models

should be sufficiently detailed to capture the effects of the major

contributing anatomical, physiological and pharmacological processes.

However, once an understanding of the system is gained, the model

should be simple enough to allow interpretation of its results. Whereas

most modeling efforts culminate with reproduction of some subset of

the known data, it is more valuable to use the model to try out new

experimental predictions.

In general, the advantage of incorporating more biological detail into a

model is the level of detail at which predictions can be made. The major

disadvantage is the increase in model complexity as it becomes more

realistic.


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The choice of level always depends upon the particular problem,

however, for many problems; a safe choice may be to construct the

model at the level of integrate-and-fire units. Such units sum their

inputs and generate an individual spike of activity whenever the firing

threshold is exceeded. A growing body of evidence suggests that the

temporal dynamics of cell activity is critical to neural function.

Perhaps the greatest challenge to the computational approach is to

begin to explain how functional behavior emerges from the operation of

cellular-level processes. This approach may reveal unsuspected

common mechanisms operating in different disease processes (relations

between Parkinsonism and Schizophrenia, or related problems in

Alzheimer’s disease, epilepsy and dyslexia). The goal is to move from the

current situation, in which the “standard model” for a disease process is

a flow chart of interconnections between brain regions, to a conceptual

model that integrates, through simulations, the wealth of information at

the molecular, cellular, network, systems and behavioral levels.


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Machine vision system for identification

of diseases in mulberry plants: A Review

Chaithra D R

PG Department of Computer Science, JSS College of Arts, Commerce and Science, Ooty Road, Mysore-25

Abstract

Machine vision has many applications in day to day life. Specially, the

research community has given importance to the field of agriculture to

develop machine vision based systems. In this direction, we are going to

present a overview of development of machine vision system for

identification of diseases in mulberry plants specially leaves. The work

carried out in this and similar area is presented in this paper. Along

with this, the challenges need to be addressed for development of

machine vision based system for identification of diseases in mulberry

plants is also presented in this paper.

Keywords: Machine vision system, mulberry plant, disease identification

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Introduction

Human vision means seeing of object or image or any picture, he can

take decisions about those. In the same way computer can also having

vision power to take decisions. The human can only give that power to

the system. Computer vision is a field that includes methods for

acquiring, processing, analysing, and understanding images and in

general, high-dimensional data from the real world in order to produce

numerical or symbolic information, e.g., in the forms of decisions.

The machine vision system has applications in different variety fields

such as medical field, agriculture field, industries field, biometrics,

military field, banking application and so on. This system has major

applications in agriculture. Classification of plants means identify and

classify the plant in given set of different species of plants is one

application. The other application is identification of diseases in crops.

Another application is identification of medicinal plant. Identification of

leaves of different plant is also one application and so on.

The dramatic and worldwide spread of plant diseases is the driving force

and motivation in the development of machine vision systems to identify

these diseases. The naked eye observation of experts is the main

approach adopted in practice for detection and identification of plant

diseases. However, this requires continuous monitoring of experts

which might be prohibitively expensive in large farms. Further, in some

developing countries, farmers may have to go long distances to contact

experts, this makes consulting experts too expensive and time

consuming. There is a need for systems that can help crop producers

and farmers, particularly in remote areas, to identify early symptoms of

plant disease by means of analyses of digital images of crop samples.


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The success of machine learning for image pattern recognition also

suggests applications in the area of identification of plant

diseases looking for fast, automatic, less expensive and accurate

method to detect plant disease cases is of great realistic significance.

Disease identification in plants takes prominent role in the field of

agriculture. The different diseases are causes to plants from different

reasons. Because of disease causing agents like virus, fungi, bacteria,

because of flying insects, because of deficiency of nutrition in soil such

as iron, nitrogen, zinc, potash and so on. The diseases are affected at

different region of the plant as leaves, stems, and roots so on. These

different affected parts of the plants are given to the system, the system

that identifies the diseases.

Sericulture plays a major role in the field of agriculture in India. This is

the commercial unit in India. In this field mulberry plant takes major

part. Mulberry plant is hard, perennial, deep rooted plant. There are

several varieties of mulberry plants. In Karnataka there are species are

there. They are MorusAlba MorusIndica. Mulberry leaves are only food

that the silkworm would eat. These are used in medical field to make

mulberry syrup. These leaves contains calcium, iron, fiber, Full of

potassium, magnesium, and other minerals. So this is used for

medicinal purpose.

Mulberry is affected by several diseases caused by fungi, bacteria,

mycoplasma, virus, and nematodes. The diseases are also caused by


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flying insects.The incidence and loss due to these diseases vary with

season, variety, and cultivation practices.

Fungal diseases are leaf spot caused by cercosporamoricola, the

Symptoms is Irregular brownish spots appear on the leaves, Powdery

mildew is caused by phyllactinacorylea karst, symptom is Mildew spots

appear on the under surface of leaves, Leaf rust is caused by Aecidium

Mori or CeroteliumFici symptoms is pathogen affects the woody portion

which results in swelling and deformity.Viral disease is

Mulberry leaf mosaic disease: caused by virus transmitted by grafting or

by insect vectors, common symptom is wrinkling of leaves mostly the

ventral surface of leaf.The diseases are caused by insects are, Tukra

caused by Mealy Bug and the symptom is Malformation of apical tips,

wrinkled dark green leaves, Leaf Roller the symptom is rolling and

binding of leaves on the apical portion of the plant. The Disease due

deficiency of iron, nitrogen and zinc -leaf turns to yellow.

1. Literature Survey

Guru et al., proposed a machine vision system for classification of

Tobacco leaves of ripe, unripe and overripe. They use 244 images of

tobacco leaves. They use CIELAB color space model in matlab to

segment the leaf and K-NN as classifier to classify the leaves. They use

textural features. Out of 244 images they got 83 samples are unripe,

102 samples are ripe and 59 samples are over ripe. Here they studied 3

models, such as Gray Level Texture Patterns (GLTP), Local Binary

Patterns (LBP) and Local Binary Pattern Variance (LBVP). Here GLTP

model has 80% of accuracy. It has highest performance compare to

other models.


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Gulhane andGurjar, worked on identification of diseases in cotton

plant. They collect the dataset of 117 images of cotton crops. They use

Box counting algorithm and Support vector machine as classifier. They

got 83% of accuracy in use of 53 features, selecting of 45 features gives

best of 93.1% of accuracy(1).

Camargo and Smith, (2009) work on the classification of leaves of

different plant species. They use 20 different leaf species 100 for each

leaf species. They use Singular value decomposition method and Back

Propagation Neural Network classifier as classifier. They got 98.9%

accuracy in BPNN classifier.

Rumpfa et al., (2010) inoculated and 15 non inoculated sugar beet

leaves to detect the Early detection and classification of plant

diseases. They use SPAD values and decision tree (DT), ANNs, SVI as

different classifiers. They got DT-95.33%, ANNs-96.63%, SVIs-97.12%

accuracy for Cercospora leaf spot diseases. Accuracy of SVIs less than

ANNs andthat is less than Decision Tree classifier.

Chen et al., (2002) use 20 diseases samples and 25 non disease

samples for detect the disease image. They use 20 diseases samples and

25 non disease samples. Here Fuzzy feature selection approach-fuzzy

curves(FS) fuzzy surface (FS) methods and Neural Network technique,

SVM as classifier. They got 90.5% accuracy.

Mallikarjuna and Guru worked on performance evaluation of

segmentation and classification of tobacco seedling diseases. They

extracted 950 lesion areas from 120 infected leaves and 50 uninfected

areas are used. They use 4 texture features. They are uniformity,

entropy, smoothness, coarseness. They use Probability Neural


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Network(PNN) as classifier and CIELAB color model in matlab. Here they

measures the performance of segmentation by evaluating the measures

as Dice Coefficient (DC), Error Rate (ER), Measures of Overlapping

(MOL), Measure of Under Segmentation (MUS), Measure of Over

Segmentation (MOS), Precision (P), Recal (R). They proposed

segmentation algorithm has high performance compare to previous one.

Guru et al., proposed a segmentation and classification of tobacco

seedling diseases. They use segment lesion areas from leaf of tobacco

seedling and probabilistic neural network as classifier. They use

statistical texture features are smoothness and coarseness to classify

the diseases of tobacco. The diseases are frog eye spot, anthracnose.

First the leaf is transformed to B-channel gray scale image. They use

750 lesion areas. Out of these 500 are anthracnose and 200 are frog-

eye spot and 50 are uninfected areas. They got the accuracy of 85.78%

of anthracnose, 82 % of frog eye spot and 98% of uninfected areas from

gray scale level co-occurrence matrix and first order statistical texture

features.

Challenges

1. Collecting dataset and processing: We select three different

diseases as three class, they are Powdery Mildew, Tukra, Deficiency of

–zn and –fe. For class Tukra 155 images, for Powdery Mildew 84

images, Deficiency of –zn and –fe 102 images are collected and

Removing of noises or unnecessary part of the images using matlab

function.

2. Feature extraction: Leaf spots, the color of image, change of shape

of image are considered the important units indicating the existence

disease. These are the different features of identification of disease.


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3. Classification: We choose any classification technique to classify

the image that belongs to any of 3 disease class.

Testing can be done through different images. To generating a dataset

as training dataset and testing dataset we use Cross validating

technique or Boot strapping technique. The output of this work

identifies the input image that belongs to the any of three classes with

the label as disease name.

Conclusion

Development of machine vision system for mulberry leaves is a

challenging task. In this paper we have discussed stages involved in

design of a pattern recognition system. The work carried out in this

direction has been discussed in brief. The challenges involved in

design of such system is also been discussed in this paper.

References

Camargo A. and Smith J.S., 2009. Image pattern classification

for the identification of disease causing agents in plants. Computers and Electronics in Agriculture, vol. 66, pp. 121–

125.

Chen Y.R., Chao K., and Kim M.S., 2002.Machine vision

technology for agricultural applications. Computers and Electronics in Agriculture, vol. 36, pp. 173-191.

Gulhane V.A., and Gurjar A.A. Detection of Diseases on Cotton

Leaves and Its Possible Diagnosis.

Guru D.S., Mallikarjuna P.B and ManjunathS. Segmentation and Classification of Tobacco Seedling Diseases

Guru D.S., Mallikarjuna.P.B., Manjunath S and Shenoi M.M,

2012 Intelligent Automation and Soft Computing, Vol. 18, No.

5, pp. 577-586


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Mallikarjuna P.B., Guru D.S., 2011, Performance Evaluation of

Segmentation and Classification of Tobacco Seedling Diseases. International Journal of Machine Intelligence Vol. 3, pp. 204-211

Rumpfa T.,Mahleinb A.K., Steinerb U., Oerkeb E.C., Dehneb H.W., Plümera L.,Rumpfa T., Steinerb U., OerkebE.C.,

DehnebH.W., and Plümera L., Early detection and classification of plant diseases with Support Vector Machines

based on hyper spectral reflectance., 2010. Computers and Electronics in Agriculture, vol. 74, pp. 91–99.


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Recognition of Image inside Multiple

Images

Mr. Rajesh K M 1, Dr. Manjunath Rao L 2

1Research Scholar, CMJ University, Shilong, Meghalaya State, India

ABSTRACT

Visual Cryptography is one kind of image encryption. It is different from

traditional cryptography, because it does not need complex computation

to decrypt. In current technology, most of visual cryptography are

embedded a secret using two shares is limited. Visual Cryptography is

based on cryptography where n images are encoded in a way that only

the human visual system can decrypt the hidden message without any

cryptographic computations when all shares are stacked together. This

paper presents an improved algorithm based on Chang’s and Yu visual

cryptography scheme for hiding a colored image into multiple colored

cover images. This scheme achieves lossless recovery and reduces the

noise in the cover images without adding any computational complexity.

KEYWORDS: Image processing, visual Cryptography, secret

sharing.

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I. INTRODUCTION:

Visual cryptography, introduced by Naor and Shamir in 1995[2], is a

new cryptographic scheme where the ciphertext is decoded by the

human visual system. Hence, there is no need to any complex

cryptographic computation for decryption. The idea is to hide a secret

message (text, handwriting, picture, etc…) in different images called

shares or cover images. When the shares (transparencies) are stacked

together in order to align the sub pixels, the secret message can be

recovered. The simplest case is the 2 out of 2 scheme where the secret

message is hidden in 2 shares, both needed for a successful decryption

[2]. This can be further extended to the k out of n scheme where a

secret message is encrypted into n shares but only k shares are needed

for decryption where k ≤ n. If k-1 shares are presented, this will give no

information about the secret message. Naor and Shamir applied this

idea on black and white images only. Few years later, Verheul and

Tilborg [4] developed a scheme that can be applied on colored images.

The inconvenient with these new schemes is that they use meaningless

shares to hide the secret and the quality of the recovered plaintext is

bad. More advanced schemes based on visual cryptography were

introduced in [1,3,5], where a colored image is hidden into multiple

meaningful cover images. Chang et al. [3] introduced in 2000 a new

colored secret sharing and hiding scheme based on Visual

Cryptography schemes (VCS) where the traditional stacking operation of

subpixels and rows interrelations is modified.[5] This new technique

does not require transparencies stacking and hence, it is more

convenient to use in real applications. However, it requires the use and

storage of a Color Index Table (CIT) in order to losselessly recover the


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secret image. CIT requires space for storage and time to lookup the

table. Also, if number of colors c increases in the secret image, CIT

becomes bigger and the pixel expansion factor becomes significant

which results in severe loss of resolution in the camouflage images.

Chang and Yu introduced in [1] an advanced scheme for hiding a

colored image into multiple images that does not require a CIT. This

technique achieves a lossless recovery of the secret image but the

generated shares (camouflage images) contain excessive noise. Here we

can introduces an improved scheme based on Chang’s technique in

order to enhance the quality of the cover images while achieving lossless

recovery and without increasing the computational complexity of the

algorithm.

II. DEVELOPMENT:

Chang’s et al. Algorithm

Chang et al. proposed in 2002 a new secret color image sharing scheme

[1] based on modified visual cryptography. The proposed approach uses

meaningful shares (cover images) to hide the colored secret image and

the recovery process is lossless. The scheme defines a new stacking

operation (XOR) and requires a sequence of random bits to be generated

for each pixel.

Method description

Assume that a gray image with 256 colors constitute a secret to be

hidden. Each color can be represented as an 8- bit binary vector. The

main idea is to expand each colored pixel into m subpixels and embed

them into n shares. This scheme uses m=9 as an expansion factor. The


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resulting structure of a pixel can be represented by an nx9 Boolean

matrix S= [Sij] where (1≤

jth subpixel in the ith share has a non-white color. To recover the color

of the original secret pixel, an “XOR” operation on the stacked rows of

the n shares is performed.

1.1 Hiding Algorithm

For a 2 out of 2 scheme, the construction can be described by a

collection of 2x9 Boolean matrices C. If a pixel with color

k=(k1k2…k8)2 needs to be shared, a dealer randomly picks an integer r

between 1 and 9 inclusively as well as one matrix in C. The

construction is considered valid if the following

conditions are satisfied:

Note that the number of 1’s in the first row of S must exceed the

number of 0’s by one.

Steps of the Algorithm

Take a colored secret image IHL of size HxL and choose any two

arbitrary cover images O1HL and O2HL of size HxL

Scan through IHL and convert each pixel Iij to an 8- bits binary string

denoted as k=(k1k2…k8) 2


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According to rp and k for each pixel, construct S to satisfy equation (1)

Scan through O1 and for each pixel of color K1p , arrange the row “i” in

S as a 3x3 block B1p and fill the subpixels valued “1” with the color

K1p

Do the same for O2 and construct B2p. The resulting blocks B1p and

B2p are the subpixels of the Pth pixel after the expansion.

After processing all the pixels in IHL, two camouflage colored images

O1’ and O2’ are generated. In order to losselessly recover IHL, both O1’

and O2’ as well as a sequence of random bits R={r1, r2, … , rI| } are

needed.

Figure 1 describes the (2,2) scheme for hiding one pixel. This

process is repeated for all pixels in IHL to construct both

camouflage images O1’ and O2’.

1.2 Recovering Algorithm

In order to recover the secret image in a 2 out of 2 scheme, both

camouflage images O1’, O2’ as well as the string of random bits R are

required for the recovery process (Fig.2). The camouflage images are t

time bigger than IHL due to the expansion factor of subpixels.


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Extract the first 3x3 blocks V1r and V2r from both camouflage images

O1’ and O2’, respectively.

Re-arrange V1r and V2r in a 2x9 matrix format Sr

Select the first random bit rp corresponding to the first encrypted pixel

Input Sr and rp to the F(.,.) function corresponding to equation (1).

Recover kp , the first pixel in IHL

Repeat for all 3x3 blocks in O1’ and O2’

2. Improved image generation schemeIn this section, we introduce a

modification of Chang's algorithm to generate better quality camouflage

images. Most of the modifications are applied to the subpixel expansion

block described in the next section.

2.1 Hiding Algorithm

Before subpixel expansion, add one to all pixels in the cover images

and limit their maximum value to 255. This ensures that no “0” valued

pixels exist in the images. When the images are expanded, replace all

the 0’s in S0, S1 by values corresponding to k1-1 in B1 and k2-1 in B2

(Figure 3) instead of leaving them transparent. Also, adjust all pixel

values to be between 0-255.


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2.2 Decryption algorithm

To recover the secret image, both camouflage images O1’ ,O2’ and

the string of random bits R are required.


Take all regions of size txt in the camouflage images

Re-structure the square matrices as 1xm vectors

Scan through the 9 subpixels in the vector and note the coordinates of

the K1 and the K1-1 colors previously encrypted

Count the number of k and k-1 pixels in the processed vector, denoted

as countk-1, countk, respectively.

If countk-1 < countk , the transparent pixel is color k-1, otherwise, set

it to k

Use the K1 and K2 colors to find the secret pixel using the F(.,.) function

and the random number previously transmitted

Repeat for all txt block pixels in the camouflage images

III. CONLUSION:


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This paper presented a new technique based on Chang et al. algorithm

[5] to hide a color secret image into multiple colored images. This

developed method does not require any additional cryptographic

computations and achieves a lossless recovery of the secret image. In

addition, the camouflage images obtained using the modified algorithm

look less susceptible of containing a secret message than the ones

obtained using the original method.

VI. REFERENCES:

[1] Chang, C. C. and Yu. T. X., Sharing a Secret Gray Image in

Multiple Images, in the Proceedings of International Symposium on Cyber Worlds: Theories and Practice, Tokyo, Japan, Nov. 2002, pp.230-237.

[2] M.Naor and A. Shamir, Visual cryptography. Advances in

Cryptology EUROCRYPT ’94. Lecture Notes in Computer Science, (950):1–12, 1995

[3] C. Chang, C. Tsai, and T. Chen, A new scheme for sharing secret color images in computer network. In the Proceedings of International Conference on Parallel and Distributed Systems,

pages 21–27, July 2000.

[4] E.Verheul and H. V. Tilborg., Constructions and properties of k out of n visual secret sharing schemes. Designs, Codes and Cryptography, 11(2):179–196, 1997.

[5] C. Yang and C. Laih., New colored visual secret sharing schemes. Designs, Codes and Cryptography, 20:325–335,2000.

[6] G. Ateniese, C. Blundo, A. D. Santis, and D. Stinson. Visual

cryptography for general access structures. Information and Computation, 129(2):86–106, 1996.

[7] R. J. Hwang and C. C. Chang, “Some Secret Sharing Schemes and Their Applications,” PhD. dissertation of National Chung Cheng University, Taiwan, 1998.


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Interpretation of Indian Classical Mudras: A Pattern Recognition

Approach

Manikanta P PG Department of Computer Science,

JSS College for Arts, Commerce and Science, Ooty road, Mysore-25. [email protected]

Abstract

This project deals with the detection and recognition of hand gestures.

Images of the hand gestures are taken using a digital camera and

matched with the images in the database and the best match is

returned. Gesture recognition is one of the essential techniques to build

user-friendly interfaces. For example, a robot that can recognize hand

gestures can take commands from humans, and for those who are

unable to speak or hear, having a robot that can recognize sign

language would allow them to communicate with it. Hand gesture

recognition could help in video gaming by allowing players to interact

with the game using gestures instead of using a controller. However,

such an algorithm needs to be more robust to account for the myriad of

possible hand positions in three-dimensional space. It also needs to

work with video rather than static images. That is beyond the scope of

our project.

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INTRODUCTION

Mudra is a Sanskrit word, meaning sign or seal. It is gesture or

position, usually of the hands,, Mudras locks and guides energy flow

and reflexes to the brain by curling, crossing, stretching, and touching

the fingers and hands. We can talk to the body and mind as each area

of the hand correspond to a certain part of the mind or body.

Gesture is a form of non-verbal communication in which visible bodily

actions communicate particular messages, either in place of speech or

together and in parallel with words. Gestures include movement of the

hands, face, or other parts of the body.

There are two categories of gestures: static and dynamic. A static

gesture is a particular hand configuration and pose, represented by a

single image. A dynamic gesture is a moving gesture, represented by a

sequence of images. We focus on the recognition of static gestures,

although our method generalizes in a natural way to dynamic gestures.

For the broadest possible application, a gesture recognition algorithm

should be fast to compute.

Computer recognition of hand gestures may provide a more natural

human-computer interface, allowing people to point, or rotate a CAD

model by rotating their hands. Interactive computer games would be

enhanced if the computer could understand players' hand gestures.

Gesture recognition is useful for processing information from humans

which is not conveyed through speech or type. As well, there are various

types of gestures which can be identified by computers.

Sign language recognition: Just as speech recognition can transcribe

speech to text, certain types of gesture recognition software can

transcribe the symbols represented through sign language into text.


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Directional indication through pointing: Pointing has a very specific

purpose in our society, to reference an object or location based on its

position relative to ourselves. The use of gesture recognition to

determine where a person is pointing is useful for identifying the

context of statements or instructions. This application is of particular

interest in the field of robotics.

RELATED WORK

Hassan et al (2012), applied multivariate Gaussian distribution to

recognize hand gestures using non geometric features. The input hand

image is segmented using two different methods; skin color based

thresholding techniques. Some operations are performed to capture the

shape of the hand to extract hand feature; the modified Direction

Analysis Algorithm are adopted to find a relationship between statistical

parameters (variance and covariance) from the data, and used to

compute object (hand) slope and trend by finding the direction of the

hand gesture.

Li (2003),recognized hand gestures using fuzzy c-means clustering

algorithm for mobile remote application. He used FCM algorithm

clustering for the classification Gestures. The system implemented

under intricate background and invariant light Conditions. The system

implemented with recognition accuracy of 85.83%.

Kulkarni and Lokhande (2010), recognize static posture of American

Sign Language using neural networks algorithm. The input image are

converted into HSV color model, resized into 80x64 and some image

preprocessing operations are applied to segment the hand from a

uniform background, features are extracted using histogram technique

and Hough algorithm. Feed forward Neural Networks with three layers


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are used for gesture classification. 8 samples are used for each 26

characters in sign language, for each gesture, 5 samples are used for

training and 3samples for testing, the system achieved 92.78%

recognition rate.

Stergiopoulou and Papamarkos (2009), suggested a new Self-Growing

and Self-Organized Neural Gas (SGONG) network for hand gesture

recognition. For hand region detection a color segmentation technique

based on skin color filter in the Y Cb Cr color space was used, an

approximation of hand shape morphology has been detected using

(SGONG) network; Three features were extracted using finger

identification process which determines the number of the raised

fingers and characteristics of hand shape, and Gaussian distribution

model used for recognition.

Trigo and Pellegrino (2010), used geometric shape descriptors for

gesture recognition. A webcam used to collect database image and

segmented it manually. Several experiments were performed and each

experiment contains one or more groups of the three features group

defined; invariant moments group with seven moments; K-curvature

group with the features: fingers number, angle between two fingers, and

distance-radius relation; and geometric shape descriptors group with

the features; aspect ratio, circularity, spreadness, roundness and

solidity. Multi-layer perception MLP used for classification. The

geometric shape descriptor group has the best performance

classification.

Freeman and Michal (1995), presented a method for recognition gesture

based on calculated local orientation histogram for each image. The

system consists of training phase, and running phase. For training


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phase, several histograms were stored in the computer as a features

vector, and in running phase, the features vector of the input gesture

extracted and compared with all the feature vectors stored in computer,

Euclidean distance metric used for recognized gestures.

Elmezain et al (2008) proposed a system to recognize isolated and

meaningful gestures for Arabic numbers (0 to 9). Gaussian Mixture

Model (GMM) used for skin color detection. For features extraction, the

orientation between the centroid points of current frame and previous

frame were determined by vector quantization. The hand motion path

recognized using different HMM topologies and BW algorithm. The

system relied on zero codeword detection models to recognize the

meaningful gestures from continuous gestures.

Kouichi and Hitomi (1999), presented posture recognition system using

neural network to recognize 42 alphabet finger symbols, and gesture

recognition system to recognize 10 words. Back propagation Neural

Network used for postures recognition and Elman Recurrent Neural

Network for gesture recognition. The two systems were integrated in a

way that after receiving the raw data, the posture system determined

the sampling start time of the input image, and if it decided to be a

gesture then it sent to the gesture system.

METHODOLOGY

In this work we are trying to automate mudra recognition. Mudra is

very important in dance and also common people, the figure1 shows the

block diagram of this work and explained as follows.

Figure 1: Block diagram of the proposed model

We take hand images from camera with black background, and we

converted color images into gray scale images after we resize the images


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and then we converted into Black & White format. After collecting the

images we extracted features are as follows,

A. SHAPE FEATURES:

We extract following shape features namely,

Area: It specifies the actual number of pixels in the region. (This value

might differ slightly from the value returned by bwarea, which

weights different patterns of pixels differently).

Euler number: It specifies the number of objects in the region minus

the number of holes in those objects. This property is supported only

for 2-D input label matrices.

Orientation: It specifies the angle (in degrees ranging from -90 to 90

degrees) between the x-axis and the major axis of the ellipse that has

the same second-moments as the region. This property is supported

only for 2-D input label matrices.

Extent: It specifies the ratio of pixels in the region to pixels in the

total bounding box. Computed as the Area divided by the area of the

bounding box.


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Perimeter: It specifies the distance around the boundary of the region.

regionprops computes the perimeter by calculating the distance

between each adjoining pair of pixels around the border of the region.

If the image contains discontiguous regions, regionprops returns

unexpected results.

Convex area: It specifies the number of pixels in 'ConvexImage'.

Filled area: specifying the number of on pixels in Filled Image.

Solidity: specifying the proportion of the pixels in the convex hull that

are also in the region. Computed as Area/Convex Area.

Eccentricity: this specifies the eccentricity of the ellipse that has the

same second-moments as the region. The eccentricity is the ratio of

the distance between the foci of the ellipse and its major axis length.

MajorAxisLength: specifying the length (in pixels) of the major axis of

the ellipse that has the same normalized second central moments as

the region.

EquivDiameter: It specifies the diameter of a circle with the same area

as the region. Computed as sqrt(4*Area/pi).

MinorAxisLength: It specifies the length (in pixels) of the minor axis of

the ellipse that has the same normalized second central moments as

the region.

B. GABOR FEATURES: It is a linear filter used for edge detection.

Frequency and orientation representations of Gabor filters are similar to

those of the human visual system.


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C. EDH FEATURES: It means Edge Direction Histogram. The basic idea

is to build a histogram with the directions of the gradients of the edges

(borders or contours). It is possible to detect edges in an image.

After feature extraction we design classifier to classification, There are

number of classifier is their, in that we have to select best classifier,

here we use KNN-classifier. And we will check the accuracy.

EXPERIMENTS AND RESULTS

We collect 25 types of Indian classical dance mudras and one mudra

consider as one class. Table 1 shows the number of class and number

of images in the class.

Table 1: Details of data set used for experimentation

No of class No of images/class

25 100-150

After collecting images we segment manually.Then extracted the

features namely Shape features, Gabor features and EDH features. Here

we use K-Nearest Neighbor classifier for classification. Before using

classifier we split the images into two parts namely Training set and

Testing set. We have used nearest neighbor Classifier using distance

measure as Euclidean distance and Table 2 shows the corresponding

accuracy under varying training and testing samples.

Table 2: Recognition accuracy of the proposed model using Euclidean

distance measure


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Training/Testing 60/40 50/50 40/60 30/70

K=1 59.4872 59.3965 55.7462 58.6370

CONCLUSION AND FUTURE WORK

We use NN classifier as classifier in this project we get result of

59.3965% Accuracy.

The designed application demonstrated the capability of a range camera

for real-time applications. Though the process seems to be promising,

further work is required to improve the segmentation speed and

different classifier have to use like SVM and Neural network etc... for to

improvement of Accuracy purpose.

Future work includes not only improvement of the designed strategy

but also taking into account more challenges such as dynamic gestures

involving both hands and/or multiple cameras. Our final objective

involves gestures with a high degree of freedom; which may require

detection of fingers and articulated hands.

REFERENCES

Elmezain M., Ayoub A., and Bernd M., 2008, HIDDEN MARKOV

MODEL BASED ISOLATED AND MEANINGFUL HAND GESTURE RECOGNITION, World Academy of Science, Vol.41, pp.393-400.

Freeman W.T and Michal R., 1995 ORIENTATION HISTOGRAMS OF

HAND GESTURE RECOGNITION, International Workshop on Face and Gesture Recognition, Zurich.

Hasan M.M and Mishra P.K, 2012 FEATURE FITTING USING MULTIVARIATE GAUSSIAN DISTRIBUTION FOR HAND GESTURE


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RECOGNITION, International Journal of Computer Science and Emerging , Vol.3(2).

Kouichi M, and Hitomi T, 1999, GESTURE RECOGNITION USING

RECURRENT NEURAL NETWORKS,ACMSIGCHI conference on human factors in computing system, pp.237-242.

Kulkarni V.S and Lokhande .S.D , 2010, APPEARANCE BASED

RECOGNITION OF AMERICA SIGN LANGUAGE USING GESTURE SEGMENTATION, IJCSE,Vol.2(3), pp 560-565.

Li X, 2003, GESTURE RECOGNITION BASED ON FUZZY C-MEANS

CLUSTERING ALGORITHM, Department of Computer Science. The University Tennessee Knoxville.

Stergiopouolou .E and Papamarkos .N, 2009, HAND GESTURE USING A NEUTRAL NETWORK SHAPE FITTING TECHNIQUE,

Elsevier Engineering Application of Artificial Intelligence, Vol.22(8), pp1141-1158.

Trigo T.R and Pellegrino R.M,2010, AN ANALYSIS OF HAND GESTURE CLASSIFICATION, IJIP, Vol.6,No.1,pp 635-646.


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Current Challenges in Plagiarism

Detection

Nagaraju L J

PG Department of Computer Science

JSS College of Arts, Commerce and Science, Ooty Road, Mysore [email protected]

Abstract

Plagiarism detection can be divided in external and intrinsic methods.

External plagiarism detection require the references documents for

finding plagiarism, but intrinsic plagiarism detection is based on

discrepancies in style within a suspicious document and it does not use

any references. Our work is completely focused on external plagiarism

detection. For document matrix representation almost all researchers

use the Vector Space Model which has the limitation of not preserving

the order of terms which is essential in preserving the actual meaning of

the document. In this project we use status matrix representation

which is capable of preserving the Order of terms. And for finding

plagiarism we use order of frequencies of words present in both source

and suspicious document, and Plagiarized lines or passages are

detected by analyzing the Status Matrices.

Keywords: Plagiarism Detection, Status Matrix.

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Introduction

Plagiarism defined as the theft of intellectual property (Meyer et al.,

2006), or act of fraud or to use (Another’s production) without crediting

the source. There exist different forms of plagiarism, ranging from

simply copying and pasting original passages to more elaborate

paraphrased and translated plagiarism. Anecdotal evidence and studies

such as (Sheard et al., 2002) strengthen the suspicion that plagiarism

is on the rise, facilitated by new media such as the World Wide Web.

Growing information sources ease plagiarism while plagiarism

prevention and detection become harder. To combat these problems the

manual method is very expensive and not possible to detect the

plagiarism for big datasets (documents), so we need automated

plagiarism detector.

Plagiarism detection is the process of locating instances of plagiarism

within a work or document. Plagiarism detection split into two tasks:

external plagiarism detection and intrinsic plagiarism detection.

External plagiarism detection deals with the problem of finding

plagiarized passages in a suspicious document based on a reference

corpus. Intrinsic plagiarism detection does not use external knowledge

and tries to identify discrepancies in style within a suspicious document

(Zechner et al., 2009).

External plagiarism detection is the approach where suspicious

documents are compared against a set of possible references. From

exact document copy, to paraphrasing, different levels of plagiarism

techniques can been used in several contexts (Eissen et al., 2006).


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External plagiarism detection relies on a reference corpus composed of

documents from which passages might have been plagiarized. A

passage could be made up of paragraphs, a fixed size block of words, a

block of sentences and so on. A suspicious document is checked for

plagiarism by searching for passages that are duplicates or near

duplicates of passages in documents within the reference corpus. An

external plagiarism system then reports these findings to a human

controller who decides whether the detected passages are plagiarized or

not (Zechner et al., 2009).

The major applications of Plagiarism detection is to detect and avoid the

plagiarism in, project reports and assignments, which are submitted by

students in the college and in the same manner we can also detect the

plagiarized text in, World Wide Web, newly published research papers,

books, journals and magazines.

From the literature survey it is understood that, almost all works are

based on VSM which has the limitation of not preserving the order of

terms which is essential in preserving the actual meaning of the

document. This is the most challenging task in information retrieval. As

the size of the document increase the number of terms also increases

which increase the dimension of the feature vector. Vector space

representation scheme will have sparse matrix which is a bottleneck for

analysis.

LITERATURE SURVEY

There are many people works on Plagiarism detection, in this section we

briefly discuss about some related works on plagiarism detection. It

covers both external and intrinsic plagiarism detections.

External Plagiarism Detection


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Zechner et al. introduce their model based on nearest neighbor

search in a high dimensional term vector space. This model contain

three steps, in the first stage Vectorization of the passages of each

document in the reference corpus means all

reference documents are represented in matrix form using Vector

Space Model. Then partitioning the reference corpus vector space and

calculate the centroid for each partitions using k-means algorithm.

Additionally we store a sorted list of similarities for each cluster,

holding the similarities between the centroid of the cluster and

the sentence vectors associated with that cluster. In the second stage

Vectorization of the passages of a suspicious document and Determine

the nearest cluster to the query sentence based on the cosine similarity

between the centroids and the query sentence. Find the position in the

sorted similarity list the query sentence would be inserted at based on

its similarity to the cluster centroid. Finding each passage’s nearest

neighbor(s) in the reference corpus vector space. Detection of

plagiarism for each suspicious document is based on its nearest

neighbor list via similarity thresholding. In the final stage they do Post

processing of the detected plagiarized passages, merging subsequent

plagiarized passages to a single block. This is accomplished by simply

checking whether sentences marked as plagiarized are in sequence in

the suspicious document. If this is the case they are merged. This is

repeated until no more merging is possible.

External plagiarism detection is similar to textual information retrieval

(IR). Given a set of query terms an IR system returns a ranked set of

documents from a corpus that best matches the query terms. The most

common structure for answering such queries is an inverted index. An

external plagiarism detection system using an inverted index indexes


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passages of the reference corpus‟ documents. For each passage in a

suspicious document a query is send to the system and the returned

ranked list of reference passages is analyzed. Such a system was

presented in (Hoad and Zobel, 2003) for finding duplicate or near

duplicate documents.

Marta et al. proposed their work, contains two steps and post

processing, in the first step, they build an information retrieval

system based on Solr/Lucene, segmenting both suspicious and

source documents into smaller texts. Then perform a search based on

bag-of-words which provides a first selection of potentially plagiarized

texts. In segmentation they choose 100 words with 50% overlap. For

each document segment used as a query, the top ranked match is

considered as a plagiarism candidate. In the second step for further

investigated. They implemented a sliding window approach that

computes cosine distances between overlapping text segments from

both the source and suspicious documents on a pair wise basis. As a

result, a similarity matrix between text segments is obtained, which is

smoothed by means of low-pass 2-D filtering. From the smoothed

similarity matrix, plagiarized segments are identified by using

image processing techniques. And finally they performed a post

processing which compacted all overlapped sections.

Another method for finding duplicates and near duplicates is

based on hashing or

fingerprinting. Such methods produce one or more fingerprints that

describe the content of a document or passage. A suspicious

document‟s passages are compared to the reference corpus based on


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their hashes or fingerprints. Duplicate and near duplicate

passages are assumed to have similar fingerprints (Brin et al., 1995).

Kasprzak & Brandejs introduced their model for automatic external

plagiarism detection.

It consists of two main phases; the first is to build the index of the

documents, while in the second the similarities are computed. This

approach uses word n-grams, with n ranging from 4 to 6, and takes into

account the number of matches of those n-grams between the

suspicious documents and the source documents for computing the

detections.

Thomas Gottron works on standard IR technologies for the

candidate selection and efficient data structures for the detailed

analysis between a suspicious and a candidate document. When

provided with a suspicious document the pre-selection component uses

the Lucene engine to retrieve candidate documents from the

source collection for a detailed comparison. The detailed analysis

then provides tuples of sequences from suspicious and candidate

documents that already represent detected plagiarized contents. A

series of post-processing filters takes care to remove pathological

cases. Prior to building the Lucene index, all non-English documents

were translated into English using Google’s translation-service.

Essentially, this corresponds to a standard cross-language indexing

approach. To be able to easily map the translated parts back onto the

original texts, they were translated in small chunks of a few

paragraphs. The information which parts of the texts correspond to

each other was stored for a later on backward resolution of character

positions in plagiarized parts.


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Another method is based on two phases; first, it executes a

plagiarism search space reduction method, and then executes an

exhaustive search to find plagiarized passages. The search space

reduction method aims at quickly identify those pair of documents that

potentially have some text in common, possibly one of them having

plagiarized from the other. For this, the method‟s general tactics are to

remove stop words, and consider word 4-grams. If two documents have

at least two word 4-grams coincidences close enough as to be in the

same paragraph, the documents are given to the next phase. Otherwise

the pair is discarded (Oberreuter et al., 2010).

Intrinsic Plagiarism Detection

Intrinsic plagiarism detection only recently received attention from the

scientific community. It was first introduced in (Meyer zu Eissen and

Stein, 2006) and defined as detecting plagiarized passages in a

suspicious document without a reference collection or any other

external knowledge. A suspicious document is first decomposed into

passages. For each passage a feature vector is constructed.

Features are derived from stylometric measures like the average

sentence length or the average word length known from the field

of authorship analysis. These features have to be topic independent so

as to capture the style of an author and not the domain she writes

about. Next a difference vector is constructed for each passage that

captures the passages deviation from the document mean vector. Meyer

zu Eissen and Stein (2006) assume that a ground truth is given,

marking passages actually from the author of the suspicious

document. A model is then trained based on one-class classification,

using the ground truth as the training set. The model is then used to


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determine which passages are plagiarized. However, it is not clear how

the ground truth is derived from a suspicious document when no

information about the document is known beforehand.

Zechner et al. present their work; it based the ideas presented in Meyer

zu Eissen and Stein, first they determine whether passages in a

suspicious document are plagiarized based only on changes in style

within the document. An author‟s style is also of importance in the field

of authorship classification. Both problems rely on so called stylometric

features. These features should be topic and genre independent and

reflect an author’s style of writing. Changes of style within a document

can be detected by various methods. They choose a simple outlier

detection scheme based on a vector space spanned by various

stylometric features. This system is composed of 3 stages: In the first

stage Vectorization of each sentence in the suspicious document.

And in the second step Determination of outlier sentences based on the

document’s mean vector. And finally do the post processing of the

detected outlier sentences.

Conclusion

From the literature survey it is understood that plagiarism detection is

a challenging issue. Comparing external and internal plagiarism,

internal plagiarism is more challenging as there are no reference

documents available. With respect to representation conventional vector

space models which work based on word frequency will not provide the

actual semantic information which is very essential in plagiarism

detection. In this direction the work has to be carried out.

References


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Brin S., Davis J and Garcia-Molina H., 1995. Copy

detection mechanisms for digital documents. In ACM International Conference on Management of Data.

Dinesh R., Harish B.S., Guru D.S and Manjunath S., 2009. Concept of Status Matrix in Classification of Text Documents.

Hoad, Timothy C., and Justin Zobel., 2003. Methods for

identifying versioned and plagiarized documents. J. Am. Soc. Inf. Sci. Technol., Vol.3, No.54, pp.203–215.

Kasprzak J. and Brandejs M., 2010. Improving the reliability of

the plagiarism detection system: Lab report for pan at clef 2010.

Meyer zu Eissen, S., Stein B. and Kulig M., 2006. Plagiarism

detection without reference collections, pp.359–366.

Oberreuter G., L‟Huillier G., Ríos S.A. and Velásquez, J.D., 2010.

Finding approximated segments of n-grams for document copy detection: Lab report for pan at clef 2010.

Sheard, Judy, Dick M., Markham S., Macdonald I., and Walsh

M., 2002. Cheating and plagiarism: perceptions and practices of first year it students. SIGCSE Bull., Vol.3, No.34, pp.183–187.

Zechner M., Muhr M., and Kern R., 2009. External and

Intrinsic Plagiarism Detection Using Vector Space Models, 3rd PAN workshop.


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A Mathematical Overview of Vision Processing

Mr. Ashwin Kumar H N1, Dr. Manjunatha Rao L2 1Research Scholar, CMJ University, Meghalaya, India

Abstract

Vision Processing for Real-time 3-D Data Acquisition Based on Coded

Structured Light system provides an idea for real-time acquisition of 3-

D surface data by a specially coded vision system. To achieve 3-D

measurement for a dynamic scene, the data acquisition must be

performed with only a single image. A principle of uniquely color-

encoded pattern projection is proposed to design a color matrix for

improving the reconstruction efficiency. The matrix is produced by a

special code sequence and a number of state transitions.

A color projector is controlled by a computer to generate the desired

color patterns in the scene. The unique indexing of the light codes is

crucial here for color projection since it is essential that each light grid

be uniquely identified by incorporating local neighborhoods so that 3-D

reconstruction can be performed with only local analysis of a single

image. The term structured light is defined as the projection of simple

or encoded light patterns onto the illuminated scene. The main benefit

of using structured light is that features in the images are better

defined. As a result, both the detection and extraction of image features

are simplified and more robust

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CONSTRUCTION OF PATTERNS

The codes are created by sequences of color values in which two

consecutive values are different. By correctly deriving the code for a

given resolution, each row results in a pattern made of grids or stripes

to be projected. Image is saved after the projection.

PRINCIPLE OF UNIQUELY COLOR-ENCODED PATTERN:

“The matrix M should consists of the color primitives of given color set

‘P, so that there are no two identical words in the matrix. Furthermore,

every element has a color different from its adjacent neighbors in the

word”.

Let P be a set of color primitives, P={1,2,…,p} where the numbers

P={1,2,…,p} representing different colors. These color primitives are

assigned to an ‘m*n’ matrix ‘M’ to form the encoded pattern which may

be projected onto the scene. We can define a word from ‘M’ by the color

value at location (i , j) in ‘M’ and the color values of its 4-adjacent

neighbors. If (Xij) is the assigned color point at row ‘i’ and column ‘j’ in

matrix ‘M’, then the word for defining this location ‘Wij’ is the sequence

{Xij.Xij-1,Xi-1j,Xij+1,Xi+1j} where i={1,2,…,m} and j={1,2,…,n}.

.Condition 1:

We need to assign the color primitives of P to the matrix M so that

there are no two identical words in the matrix.

W={wij | wij != wkl,(i,j) != (k,l), 2 <= i,k <= (m-1), 2<= j,l <= (n-1)}

. Condition 2:


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Furthermore, every element has a color different from its adjacent

neighbors in the word.

M = {xij | xij != xi-1j, xij != xi+1j, xij != xij+1,xij != xij-1, 1 <= i<= m, 1 <=j<=

n}

PATTERN CODIFICATION

A given number of colors used to create the code, the codes are defined

as the color elements found by traversing the square matrix row by row

from the top left corner.

First with a given color set P, we try to make a longest horizontal code

sequence

Sh=[c1,c2,c3…..,cm]

where ‘m’ is the sequence length. For any adjacent color pair, it satisfies

Ci!=Ci+1,1<=i<m

and any triplet of adjacent colors, T3i=[ci,ci+1,ci+2] , is unique in the

sequence

T3i! = T3j, i! = j, 1 <=I , j<= m-2

The maximal length of the horizontal sequence ‘Sh’ is

Length (Sh)=p(p-1)(p-1)+2

CALIBRATION

The calibration process computes the intrinsic and extrinsic

parameters. This is mandatory for gathering accurate and robust 3D

measurements.


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CAMERA CALIBRATION:

The intrinsic parameters of the camera C are defined by the 3×3 matrix.

--------------- (A)

where

‘m’ is expressed in the image coordinates system (u, v), is the

ideal (undistorted) projection of the world point ‘MC’.

‘MC’ is expressed in the camera coordinates system (XC, YC,

ZC).

The parameters ‘αu’ and ‘αv’ are the scaling values in the ‘XC’

and ‘YC’ directions, respectively.

The principal point (u0, v0) is the point where the optical axis

intersects the image plane.

The parameter ‘θ’ is the skew angle between the image axes ‘u’

and ‘v’.

The parameter λ = 1 /ZC is a scale factor.

The extrinsic parameters of the camera C are defined by the

rigid displacement (R1, t1) such that

------------- (B)

where

‘R1’ is a 3×3 rotation matrix.


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‘t1’ is a 3×1 translation vector, and ‘O3’ is a 3 × 1 null

vector.

The 4 × 4 matrix ‘ESC’ represents the transformation

from the camera coordinate system to the scene

coordinate system.

PROJECTOR CALIBRATION:

The intrinsic parameters of the light projector are given by the

characteristics of the used projection pattern. The extrinsic parameter

of the projector ‘P’ are defined by the rigid displacement (R, t) such that

---- ------------ (C)

where

The 4 × 4 matrix ‘ECP‘ represents the transformation

from the projector coordinate system to the camera

coordinate system.

The rigid displacement (R2, t2) between the scene and

projector coordinate system can be analytically

computed from expression (B) and (C).

TRIANGULATION PROCESS

The triangulation is the process that determines the 3D position of a

point given its 2D positions on the perspective projections of the camera

and the projector, respectively. Given a 3D point ‘X’ expressed in the

camera coordinate system, and its two projections ‘XC‘ and ‘XP‘

expressed in normalized coordinates in camera and projector


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coordinates systems, respectively. Let ‘Yc’,’Zc’ and ‘YP’,’ZP’ are camera

and projector coordinates. If (R, t) lines the rigid transformation between

camera and projector coordinate systems, then we can express in

camera coordinate system.

X = λ XC and X = XP + μ Y P --------------- (D)

Similarly Y= λ YC and Y = YP + μ Z P, Z= λ ZC and Z =

ZP + μ P

Where

XP = ROP + t and P = RXP + t ------------

--- (E)

YP = ROP + t and P = RYP + t

ZP = ROP + t and P = RZP + t and λ = 1

/ZC.

From expression 4 we obtain the simplified system.

--------

------ (F)

The expression of resolution 6 gives

Z= XP - ZP P / XC - P or YP- ZP Y P/YC- YP

------- (G)

Finally, with the previously computed ‘Z’ value and expression 6,

the coordinates of the 3D point ‘X’ can be fully acquired.


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Conclusion

Real-time, low-cost, reliable, and accurate 3-D data acquisition is a

dream for us in the vision community. While the available technology is

still not able to reach all the features such as speed, accuracy and

processing of only single image together, this project makes a significant

progress to the goal. An idea was presented and implemented for

generating a specially color-coded light pattern, which combines the

advantages of both fast 3-D vision processing from a single image and

reliability and accuracy from the principle of structured light systems.

With a given set of color primitives, the patterns generated are

guaranteed to be a large matrix and desired shape with the restriction

that each word in the pattern matrix must be unique. By using such a

light pattern, correspondence problem is solved within a single image

and therefore, this is used in a dynamic environment for real-time

applications. Furthermore, the method does not have a limit in the

smoothness of object surfaces since it only requires analyzing a small

part of the scene and identifies the coordinates by local image

processing which greatly improves the 3-D acquisition efficiency.

REFERENCES

[1] M. Ribo and M. Brandner, “State of the art on vision-based

structured light systems for 3D measurements,” in Proc. IEEE Int. Workshop on Robotic Sensors: Robotic and Sensor Environments, Ottawa.

[2] J. Salvi, J. Pags, and J. Batlle, “Pattern codification strategies in

structured light systems,” Pattern Recognit., vol. 37, no. 4, pp. 827–849,Apr. 2004.

[3] D. Desjardins and P. Payeur, “Dense stereo range sensing with marching pseudo- random patterns,” in Proc. 4th Canada. Conf. May

2007, pp. 216–226.

[4] S. Osawa, “3-D shape measurement by self-referenced pattern

projection method,” Measurement, vol. 26, pp. 157–166, 1999.


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[5] C. S. Chen, Y. P. Hung, C. C. Chiang, J. L. Wu, and Range, “Data acquisition using color structured lighting and stereo vision,” Image Vis. Comput., vol. 15, pp. 445–456, 1997.

[6] L. Zhang, B. Curless, and S. M. Seitz, “Rapid shape acquisition using color structured light and multi-pass dynamic programming,” in Proc. IEEE 3D Data Processing Visualization and Transmission, Padova, Italy, Jun. 2002, pp. 24–36.


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Taxonomy of Multicast routing protocols for Mobile

ad-hoc networks

Mr. Jagadeesh Krishna S1, Dr. Manjunatha Rao L2 1Research Scholar, CMJ University, Meghalaya, India

ABSTRACT

Mobile nodes self-organize to form a network over radio links. A mobile

ad-hoc network (MANET) is composed of mobile nodes without any

infrastructure.. The goal of MANETs is to extend mobility into the

autonomous, mobile and wireless domains, where a set of nodes form

the network routing infrastructure in an ad-hoc fashion. The majority of

applications of MANETs are in areas where rapid deployment and

dynamic reconfiguration are necessary and wired network is not

available. These include military battlefields, emergency search, rescue

sites, classrooms and conventions, where participants share

information dynamically using their mobile devices. These applications

lend themselves well to multicast operations. In addition, within a

wireless medium, it is crucial to reduce the transmission overhead and

power consumption. Multicasting can improve the efficiency of the

wireless link when sending multiple copies of messages by exploiting

the inherent broadcast property of wireless transmission. Hence,

reliable multicast routing plays a significant role in MANETs. However,

to offer effective and reliable multicast routing is difficult and

challenging. In recent years, various multicast routing protocols have

been proposed for MANETs. These protocols have distinguishing

features and employ different recovery mechanisms. To provide a

comprehensive understanding of these multicast routing protocols and

better organize existing ideas and work to facilitate multicast routing

design for MANETs, we present the taxonomy of the multicast routing


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protocols, their properties and design features This paper aims to aid

those MANETs researchers and application developers in selecting

appropriate multicast routing protocols for their work.

Keywords: Mobile ad-hoc network (MANET); Multicast routing

protocol; Taxonomy; Mobile node; Routing table.

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INTRODUCTION

Multicasting is the transmission of packets to a group of zero or more

hosts identified by a single destination address [1]. Multicasting is

intended for group-oriented computing, where the membership of a host

group is typically dynamic that is, hosts may join and leave groups at

any time. There is no restriction on the location or number of members

in a host group. A host may be a member of more than one group at a

time. Also, a host does not have to be a member of a group to send

packets to the members in the group. In the wired environments, there

are two popular network multicast schemes: shortest path multicast

tree and core-base tree. The shortest path multicast tree method

guarantees the shortest path to each destination, but each source has

to build a tree. Therefore, too many trees exist in the network. The core-

based tree method cannot guarantee the shortest path from a source to

a destination, but only one tree is required to be constructed for each

group. Therefore, the number of trees is greatly reduced.

Currently, one particularly challenging environment for multicast is in

MANETs [2,3]. A MANET is a self-organizing collection of wireless mobile

nodes that form a temporary and dynamic wireless network established

by a group of mobile nodes on a shared wireless channel without the

aid of a fixed networking infrastructure or centralized administration. A

communication session is achieved either through single-hop

transmission if the recipient is within the transmission range of the

source node, or by relaying through intermediate nodes other-wise. For

this reason, MANETs are also called multi-hop packet radio networks.

However, the transmission range of each low-power node is limited to

each other’s proximity, and out-of-range nodes are routed through

intermediate nodes.


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Mobile nodes in MANETs are capable of communicating with each other

without any network infrastructure or any centralized

administration.Mobile nodes are not bounded to any centralized control

like base stations or mobile switching centers. Due to the limited

transmission range of wireless network interfaces, multiple hops may be

needed for one node to exchange data with another across the network.

In such a network, each mobile node operates not only as a host but

also as a router, forwarding packets for other mobile nodes in the

network that may not be within direct wireless transmission range of

each other. Each node participates in an ad-hoc routing function that

allows it to discover multi-hop paths through the network to any other

node.

Related work

As a promising network type for future mobile application, MANETs are

attracting more and more researchers [2,3]. In multicast routing

protocols field, some researches on the taxonomy of multicast routing

protocols over MANETs have been carried out. Tariq Omari et al. [4]

classify multicast routing protocols into tree-based mesh-based,

stateless, hybrid-based and flooding protocols and evaluate the

performance and capacity of multicast routing protocols for MANETs.

Two distinct on-demand multicast protocols, Forwarding Group

Multicast Protocol (FGMP) and core-assisted mesh protocol are

described in [5]. And other multicast protocols used in MANETs have

also been briefly summarized. In [6], AODV ODMRP, PBM and PAST-

DMPUMA are explained. In[7], Cordeiro et al. provide information about

the current state-of-the-art in multicast protocols for MANETs, and

compares them with respect to several performance metrics. In [7,8],

authors classify these protocols into four categories based on how


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routes are created to the members of the group: tree-based approaches,

meshed-based approaches, stateless multicast and hybrid approaches.

Multicasting routing protocols

The majority of applications for MANETs are in areas where rapid

deployment and dynamic reconfiguration are necessary and the wired

network is not available. These include military battlefields, emergency

search and rescue sites, classrooms, and conventions where

participants share information dynamically using their mobile devices.

These applications lend themselves well to multicast operation. In

addition, within a wireless medium, it is even more crucial to reduce the

transmission overhead and power consumption. Multicasting can be

used to improve the efficiency of the wireless link when sending

multiple copies of messages to exploit the inherent broadcast nature of

wireless transmission.So multicast plays an important role in

MANETs[2].

In the wired environments, there are two popular network multicast

approaches, namely, shortest path multicast tree and core-based

tree[9]. The shortest path multicast tree guarantees the shortest path to

each destination. But each source needs to build a tree. Usually, there

exist too many trees in the network, so the overhead tends to be large.

In contrast, the core-based tree constructs only one tree for each group

and the number of trees is greatly reduced. Unlike typical wired

multicast routing protocols, multicast routing for MANETs must

address a diverse range of issues due to the characteristics of MANETs,

such as low bandwidth, mobility and low power. MANETs deliver lower

bandwidth than wired networks; therefore, the information collection

during the formation of a routing table is expensive. Mobility of nodes,


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which causes topological changes of the underlying network, also

increases the volatility of network information. In addition, the

limitation of power often leads users to discon-nect mobile

units.Multicasting routing protocols have emerged as one of the most

focused areas in the field of MANETs. There are three basic categories of

multicast methods [9] in MANETs:

1. A basic method is to simply flood the network. Every node

receiving a message floods it to a list of neighbors. Flooding a

network acts like a chain reaction that can result in exponential

growth.

2. The proactive approach pre-computes paths to all possible

destinations and stores this information in the routing table. To

maintain an up-to-date database, routing information is

periodically distributed through the network.

3. The final method is to create paths to other nodes on demand.

The idea is based on a query response mechanism or reactive

multicast. In the query phase, a node explores the environment.

Once the query reaches the destination. The response phase

starts and establishes the path.

Recently, many multicast routing protocols have been newly proposed

to perform multicasting in MANETs. These include ad-hoc multicast

routing protocol utilizing increasing Id numbers (AMRIS)[10], multicast

ad-hoc on-demand vector (MAODV)[11], core assisted mesh protocol

(CAMP) [12], lightweight adaptive multicast (LAM) [13], location

guided tree (LGT) [14], on-demand multicast routing protocol (ODMRP)

[15], forwarding group multicast protocol (FGMP)[16], ad-hoc multicast


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routing (AMRoute) [17], multicast core extraction distributed ad-hoc

routing (MCEDAR) [18] and differential destination multicast (DDM)

[19]. Most of these multicast routing protocols are primarily based on

flavors of distance-vector or link-state routing plus additional

functionalities to assist the routing operations in particular ways. The

goals of all these protocols include minimizing control overhead,

minimizing processing overhead, maximizing multi-hop routing

capability, maintaining dynamic topology and preventing loops in the

networks etc.

However, many multicast routing protocols do not perform well in

MANETs because in a highly dynamic environment, nodes move

arbitrarily, thus network topology changes frequently and

unpredictably. Moreover, bandwidth and battery power are limited.

These constraints in combination with the dynamic network topology

make multicasting routing protocol designing for MANETs extremely

challenging.

Taxonomy of multicast routing protocols

To compare and analyze multicast routing protocols, appropriate

classification methods are important. Classification methods help

researchers and designers to understand the distinct characteristics of

different multicast routing protocols and find out the internal

relationship among them. Therefore, we present protocol characteristics

which are used to group and compare different approaches. These

characteristics are mainly related to the information which is exploited

for MANETs and the roles which nodes may take in the multicast

routing process.

1. Tree, mesh and hybrid multicast routing protocols


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One of the most popular methods to classify multicast routing protocols

for MANETs is based on how distribution paths among group members

are constructed. According to this method, existing multicast routing

approaches for MANETs can be divided into tree-based multicast

protocols, mesh-based multicast protocols and hybrid multicast

protocols. Tree-based multicast routing protocols can be further divided

into source-rooted and core-rooted schemes according to the roots of

the multicast trees. In a source-rooted tree-based multicast routing

protocol, source nodes are roots of multicast trees and execute

algorithms for distribution tree construction and maintenance. This

requires a source to be aware of the topology information and addresses

of all its receivers in the multicast group.Therefore, source-rooted tree-

based multicast routing protocols suffer from high traffic overhead

when used for dynamic networks. AM Route is an example for source-

rooted tree multicast routing protocol.

In a core-rooted tree multicast routing protocol, cores are nodes with

special functions such as multicast data distribution and membership

management. Some core-rooted multicast routing protocols utilize tree

structures. But unlike source-rooted tree-based multicast routing,

multicast trees are only rooted at core nodes. For different source-

rooted multicast routing protocols, core nodes may perform various

routing and management functions. Shared Tree Ad-hoc Multicast

Protocol (STAMP)[20]and Adaptive Core-based Multicast Routing

protocol (ACMP) [21] are core-based multicast routing protocols

proposed for MANETs.

Tree-based protocols provide high data forwarding efficiency at the

expense of low robustness. Their advantage is their simplicity. Their


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disadvantage is that until the tree is reconstructed after movement of a

node, packets possibly have to be dropped.

In a mesh-based multicast routing protocol, packets are distributed

along mesh structures that are a set of interconnected nodes. Route

discovery and mesh building are accomplished in two ways: by using

broadcasting to discover routes or by using core or central points for

mesh building. Mesh-based protocols perform better in high mobility

situation as they provide redundant paths from source to destinations

while forwarding data packets. However, mesh-based approaches

sacrifice multicast efficiency in comparison to tree-based

approach. Mesh-based Multicast Routing Protocol with Consolidated

Query Packets (CQMP)[22],Enhanced On-Demand Multicast Routing

Protocol (E-ODMRP)[23]and Bandwidth Optimized and Delay Sensitive

(BODS) [24]are the mesh-based multicast routing protocols

proposed for MANETs.Hybrid-based multicast routing protocols

combine the advantages of both tree and mesh-based approaches.

Hence, hybrid protocols address both efficiency and robustness. Using

this scheme, it is possible to get multiple routing paths, and duplicate

messages can reach a receiver through different paths. However, they

may create non-optimal trees with nodes mobility. Efficient Hybrid

Multicast Routing Protocol (EHMRP) [25] is an instance for hybrid-based

multicast routing protocol.

2. Proactive and reactive multicast routing protocols

Another classification method is based on how routing information is

acquired and maintained by mobile nodes. Using this method, multicast

routing protocols can be divided into proactive routing and reactive

routing. A proactive multicast routing protocol is called ‘‘table-driven”


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multicast routing protocol. In a network utilizing a proactive routing

protocol, every node maintains one or more tables representing the

entire topology of the network. These tables are updated regularly in

order to maintain up-to-date routing information from each node to

every other node. To maintain up-to-date routing information, topology

information needs to be exchanged between the nodes on a regular

basis, leading to relatively high overhead on the network. On the other

hand, routes will always be available on request. There are some typical

proactive multicast routing protocols, such as CAMP, LGT and AMRIS.

A reactive multicast routing protocol is also called ‘‘on-demand”

multicast routing protocol. Reactive protocols seek to set up routes on-

demand. If a node wants to initiate communication with a node to

which it has no route, the routing protocol will try to establish such a

route. Reactive multicast routing protocols have better scalability than

proactive multicast routing protocols. However, when using reactive

multicast routing protocols, source nodes may suffer from long delays

for route searching before they can forward data packets. ACMP and

CQMP are examples for reactive routing protocols for MANETs.

3. Evaluating capacity, architecture and location for multicast

routing protocols

Most of the multicast routing protocols assume a physically flat network

architecture with mobile nodes having homogeneous capability in terms

of network resources and computing power. In practice however, this

assumption may not often hold since there exist various types of mobile

nodes with different roles, capacities and mobility patterns. In an

architecture-based multicast routing protocol, MANETs have physically

hierarchical architectures, which are formed by different types of mobile

nodes. For example, Hierarchical QoS Multicast Routing Protocol


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(HQMRP) for MANETs builds a multicast structure at each level of the

hierarchy for efficient and scalable multicast message delivery. Self-

Organizing Map (SOM) is also a typical hierarchical architecture, which

provides a way for automatically organizing the hierarchical

architecture. In location-based multicast routing protocols, the

availability of a Global Positioning System (GPS), Bluetooth or other

locations systems easily gets geographical information of mobile nodes

when needed . Each node determines its own location through the use

of GPS or some other type of positioning service. A location service is

used by the sender of a packet to determine the location of the

destination. The routing decision at each forwarding node is then based

on the location information of its neighbors and the destination nodes

Location-based Geocasting and Forwarding (LGF), LGT and Scalable

Position-Based Multicast(SPBM) protocol are typical location-based

multicast routing protocols for MANETs.

4. Quality of service

Another protocol classification is based on metrics used for multicast

routing construction as criteria for MANETs. Most of conventional

multicast routing protocols are designed for minimizing data traffic in

the network or minimizing the average hops for delivery a packet. When

Quality of Service (QoS) is considered, some protocols may be

unsatisfactory or impractical due to the lack of resources, the excessive

computation overhead, and the lack of knowledge about the global

network state or the excessive message processing overhead. However,

some multicast routing protocols, such as LGT,AMRIS and CAMP are

designed without explicitly considering QoS. QoS multicast routing not

only requires finding a route from a source to a destination, but


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satisfying the end-to-end QoS requirement, often given in terms of

bandwidth or delay. QoS is more difficult to guarantee in MANETs than

in other types of networks, because the wireless bandwidth is shared

among adjacent nodes and the network topology changes as the nodes

move. This requires extensive collaboration between the nodes, both to

establish the routes and to secure the resources necessary to provide

the QoS. With the extensive applications of MANETs in many

domains, the appropriate QoS metrics should be used, such as

bandwidth, delay, packet loss rate and cost for multicast routing

protocols. Therefore,QoS multicasting routing protocols face the

challenge of delivering data to destinations through multi-hop routes in

the presence of node movements and topology changes. Multicast Core

Extraction Distributed Ad-hoc Routing (MCEDAR) is an example for

QoS-based multicast routing protocols for MANETs.

5. Energy efficiency

Because MANETs are a set of nodes that agree upon forming a

spontaneous, temporary network with the lack of any centralized

administration, any form of infrastructure and nodes are typically

powered by batteries with a limited energy supply, each node ceases its

function when the battery exhausts. Therefore, given the energy

constraints placed on the network’s nodes, designing energy-efficient

multicast routing protocols is an important issue for MANETs,

maximizing the lifetime of its nodes and thus of the network itself.

Minimum Weight Incremental Arborescence (MWIA), RB-MIDP and D-

MIDP are examples for energy-efficient multicast routing.

6. Network coding


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The advent of the notion of coding at the packet level, commonly called

network coding, change many aspects of networking. Given a network

with capacity constraints on links, one problem of designing multicast

routing protocols is to maximum the multicast throughput between a

source node and a set of receivers. The main advantage of using

network coding can be seen in multicast scenarios. Network coding

enables better resource utilization and achieves the max-flow which is

the theoretical upper bound of network resource utilization, by allowing

a network node, such as a router to encode its received data before

forwarding it. Each node implementing the network coding function,

receives information from all the input links, encodes it and sends the

encoded information to all output links. The coded-network lends itself,

for multicast connections, to a cost optimization which not only

outperforms traditional routing tree-based approaches, but also lends

itself to a distributed implementation and to a dynamic implementation

when changing conditions, such as mobility.

7. Reliable multicast routing protocols

In ad-hoc environments, every link is wireless and every node is mobile.

Those features make data loss easy as well as multicasting inefficient

and unreliable. Reliable multicast routing protocol becomes a very

challenging research problem for MANETs. The design of reliable

multicasting depends on the following three decisions: (1) by whom

errors are detected; (2) how error messages are signaled and (3) how

missing packets are retransmitted.

In the sender-initiated approach, the sender is responsible for the error

detection. Error messages are signaled using ACK signals sent from

each receiver. A missing piece of data at a receiver is detected if the


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sender does not receive an ACK from the receiver. In this case, the need

to retransmit a missing packet is handled by retransmitting the missing

data from the source through a unicast. When several receivers have

missing packets, the sender may decide to re-multicast the missing

packets to all receivers in the multicast group. In the receiver-initiated

approach, each receiver is responsible for error detection. Instead of

acknowledging each multicast packet, each receiver sends a NACK once

it detects a missing packet. If multicast packets are time-stamped using

a sequence number, a missing packet can be detected by a gap between

sequence numbers of the receiving packets. When the sender-initiated

approach is applied, only the sender is responsible for retransmitting

the missing packet, and the corresponding retransmitting method is

called an sender-oriented. Note that when the sender receives ACK

signals from all the receivers, the corresponding packet can be removed

from the history. There are three ways to retransmit the missing packet

when the receiver-initiated approach is used: (1) sender-oriented, (2)

neighborhood-oriented, and (3) fixed-neighborhood-oriented.

8. Overlay multicast routing protocols

In most protocols, both group members and non-members on a

tree/mesh link must maintain the multicast states to forward data

packets. Thus, multicast protocols must detect and restore link

failure,which can be a result of migrations by non-group members as

well as group members. As a result,many control messages are issued

to repair broken links. To provide data forwarding without involvement

of non-group members and to constrain the protocol states on group

members, overlay multicast protocols for MANETs enhance the packet

delivery ratio by reducing the number of reconfigurations caused by

non-group members’ unexpected migration in a tree or mesh structure.


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The advantages of overlay multicast come at the cost of low efficiency of

packet delivery and long delay. However, when constructing the virtual

infrastructure, it is very hard to prevent different unicast tunnels from

sharing physical links, which results in redundant traffic on the

physical links. Overlay multicast based on heterogeneous forwarding

(OMHF)[42]is an example for overlay multicast routing protocols for

MANETs.

9. Single and multiple source multicast routing protocols

A multicast group may contain multiple sources due to different kinds

of services or applications simultaneously provided by the networks.

Each single source multicast routing protocol induces a lot of overhead

and thus wastes tremendous network resources in multi-source

multicast environments. In multiple source multicast routing protocols,

using the clustering technique, a large network can be divided into

several sub-networks with only a few cluster heads needing to maintain

local information, thus preventing flooding of useless packets and

avoiding wasting bandwidth. To achieve efficient multicasting in a

multi-source multicast environment, the clustering technique is

employed to design an efficient multicast routing protocol for multi-

source multicasting. Multiple source routing is essential for load

balancing and offering quality of service. Other benefits of multiple

source routing include: the reduction of computing time that routers’

CPUs require, high resilience to path breaks, high call acceptance ratio

(in voice applications) and better security. +

Performance criteria


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Many multicast routing protocols are proposed for MANETs based on

different design points of view to meet specific requirements from

different application domains. There are tree different ways to evaluate

and compare the performance of multicast routing protocols for

MANETs:

1. The first one is based on user parameters and configurations,

such as the average multicast degree,the control overhead, the

average delay, the throughput and the multicast service cost .

2. The second way is comparing different multicast routing

updating methods. Multicast routing update can be done in one

of three ways: (a) Store and update: store the information in a

routing table and update it by listening to routing messages. (b)

Delete all and refresh: discard all old routes (timeout) and

start over and (c) Unicast protocol support: use the services of a

separate unicast routing protocol for route updating. In another

method, the performance of multicast routing protocols is

evaluated with different simulation tools, such as NS-2, Opnet,

Matlab.

3.With the popularity of MANETs and considering the dynamic

network features of MANETs, integrated criteria for evaluating

performance of MANETs multicast routing protocols should be

proposed to meet the different mobile application requirements

in different environments and different design targets .

Conclusion

A MANET consists of dynamic collections of low power nodes with


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quickly changing multi-hop topologies that usually composed of

relatively low bandwidth wireless link. These constraints make

multicasting in MANETs challenging. General solutions to solve these

problems are to avoid global flooding and advertising, construction of

routes on demand, and dynamically maintain memberships, etc.

Multicasting can efficiently support a wide variety of applications that

are characterized by a close degree of collaboration, typical for many

MANETs. The design of the multicast routing protocols for MANETs are

driven by specific goals and requirements based on respective

assumptions about the network properties or application area.All

protocols have their own advantages and disadvantages. Some

constructs multicast trees to reduce end-to-end latency while others

build mesh to ensure robustness. Some protocols create overlay

networks and use unicast routing to forward packets. Energy-aware

multicast protocols optimize either total energy consumption or system

lifetime of the multi-cast tree.

Future work

As mentioned earlier, research in the area of multicast over MANETs is

far from exhaustive. Much of the effort so far has been on devising

routing protocols to support effective and efficient communication

between nodes that are part of a multicast group. It is really difficult to

design a multicast routing protocol considering all the above mentioned

issues. Still, there are still many topics that deserve further

investigation:

1. Scalability. This issue is not only related to multicast in

MANETs but also with the ad-hoc itself.A multicast routing

protocol is scalable with respect to some constraints posed by

MANETs.


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2. Address configuration. In ad-hoc environments, a different

addressing approach may be required. Special care must be

taken so that other groups do not reuse a multicast address

used by a group at the same time. Node movement and network

partitioning makes this task of synchronizing multicast

addresses in a MANET really difficult.

3. Multicast service support. The multicast protocol defines

conditions for joining/leaving groups,multicast participants

should be able to join or leave groups at will. On the other hand,

service providers can be convinced to support multicast

protocols.

4. Security. How can the network secure itself from malicious or

compromised nodes? Due to the broadcast nature of the

wireless medium security provisioning becomes more difficult.

Further research is needed to investigate how to stop an

intruder from joining an ongoing multicast session or stop a

node from receiving packets from other sessions.

5. Traffic control. Both source and core-based approaches

concentrate traffic on a single node. In stateless multicast group

membership is controlled by the source, which leads to the

vulnerability of multicast protocols for MANETs. Still need to be

investigated is how to efficiently distribute traffic from a central

node to other member nodes for MANETs.

6. QoS. QoS defines a guarantee given by the network to satisfy a

set of predetermined service performance constraints for the

user in terms of end-to-end delay, jitter, and available

bandwidth. Therefore, multicast routing protocols must be

feasible for all kinds of constrained multicast applications to run


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well in a MANET.However, it is a significant technical challenge

to define a comprehensive framework for QoS support, due to

dynamic topology, distributed management and multi-hop

connections for MANETs.

7. Power control. For power-constrained wireless networks, a

crucial issue in routing and multicasting is to conserve as much

power as possible while still achieving good throughput

performance.

8. Multiple sources. Most of the existing multicast routing

protocols in ad-hoc networks are designed for single source

multicasting. However, a multicast group may contain multiple

sources due to different kinds of services or applications

simultaneously provided by the networks. Each single source

multicast routing protocol induces a lot of overhead and thus

wastes tremendous network resources in a multi-source

multicast environment.

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[4] O. Tariq, F. Greg, W. Murray, On the effect of traffic model to the performance evaluation of multicast protocols in MANET,

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tree ad-hoc multicast protocol, MILCOM 2006 (2006) 1–7,October.

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Security Approaches on Progressive Authentication Method Accessing

Multiple Information in Mobile Devices

Santhosh Kumar

Asst. Proffessor, GFGC, Hunsur

Abstract

Mobile device security has become increasingly important in mobile

computing. It is of particular concern as it relates to the security of

personal information now stored on smart-phones. Mobile devices face

an array of threats that take advantage of numerous vulnerabilities

commonly found in such devices. These vulnerabilities can be the result

of inadequate technical controls, but they can also result from the poor

security issues. Security controls are not always consistently

implemented on mobile devices. The importance of enabling security

controls on mobile devices and adopting to implement the security

approach towards authentication is the subject intended to the research

of this paper. Mobile users are often faced with a trade-off between

security and convenience. Either users do not use any security lock and

risk compromising their data, or they use security locks but then have

to inconveniently authenticate every time they use the device. Rather

than exploring a new authentication scheme, we address the problem of

deciding when to surface authentication and for which applications. We

believe reducing the number of times a user is requested to

authenticate lowers the barrier of entry for users who currently do not

use any security. Progressive authentication, the approach we propose,

combines multiple signals (biometric, continuity, possession) to

determine a level of confidence in a user’s authenticity. Based on this

confidence level and the degree of protection the user has configured for

his applications, the system determines whether access to them

http://en.wikipedia.org/wiki/Mobile_computing

http://en.wikipedia.org/wiki/Mobile_computing

http://en.wikipedia.org/wiki/Information_security

http://en.wikipedia.org/wiki/Smartphones


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requires authentication. Thus representing an attractive solution for

users who do not use any security mechanism on their devices.

------------------------ ---------------------------


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1. INTRODUCTION

1.1 Mobile devices often do not have passwords enabled. Mobile devices

often lack passwords to authenticate users and control access to data

stored on the devices. Many devices have the technical capability to

support passwords, personal identification numbers (PIN), or pattern

screen locks for authentication. Some mobile devices also include a

biometric reader to scan a fingerprint for authentication. Additionally, if

users do use a password or PIN they often choose passwords or PINs

that can be easily determined or bypassed, such as 1234 or 0000.

Without passwords or PINs to lock the device, there is increased risk

that stolen or lost phones' information could be accessed by

unauthorized users who could view sensitive information and misuse

mobile devices[15].

1.2 Two-factor authentication is not always used when conducting

sensitive transactions on mobile devices. According to studies, users

generally use static passwords instead of two-factor authentication

when conducting online sensitive transactions while using mobile

devices. Using static passwords for authentication has security

drawbacks: passwords can be guessed, forgotten, written down and

stolen, or eavesdropped. Two-factor authentication generally provides a

higher level of security than traditional passwords and PINs, and this

higher level may be important for sensitive transactions. Two-factor

refers to an authentication system in which users are required to

authenticate using at least two different "factors" — something you

know, something you have, or something you are — before being

granted access. Mobile devices can be used as a second factor in some

two-factor authentication schemes. The mobile device can generate pass

codes, or the codes can be sent via a text message to the phone.

Without two-factor authentication, increased risk exists that


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unauthorized users could gain access to sensitive information and

misuse mobile devices.

1.3 Wireless transmissions are not always encrypted. Information such

as e-mails sent by a mobile device is usually not encrypted while in

transit. In addition, many applications do not encrypt the data they

transmit and receive over the network, making it easy for the data to be

intercepted. For example, if an application is transmitting data over an

unencrypted WiFi network using http (rather than secure http), the

data can be easily intercepted. When a wireless transmission is not

encrypted, data can be easily intercepted.

1.4 Mobile devices may contain malware. Users may download

applications that contain malware. Users download malware

unknowingly because it can be disguised as a game, security patch,

utility, or other useful application. It is difficult for users to tell the

difference between a legitimate application and one containing malware.

For example, an application could be repackaged with malware and a

consumer could inadvertently download it onto a mobile device. The

data can be easily intercepted. When a wireless transmission is not

encrypted, data can be easily intercepted by eavesdroppers, who may

gain unauthorized access to sensitive information.

1.5 Mobile devices often do not use security software. Many mobile

devices do not come preinstalled with security software to protect

against malicious applications, spyware, and malware-based attacks.

Further, users do not always install security software, in part because

mobile devices often do not come preloaded with such software. While

such software may slow operations and affect battery life on some

mobile devices[28,29], without it, the risk may be increased that an

attacker could successfully distribute malware such as viruses, Trojans,

http://www.networkworld.com/topics/wireless.html

http://www.networkworld.com/topics/applications.html


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spyware, and spam to lure users into revealing passwords or other

confidential information.

1.6 Operating systems may be out-of-date. Security patches or fixes for

mobile devices' operating systems are not always installed on mobile

devices in a timely manner. It can take weeks to months before security

updates are provided to users' devices. Depending on the nature of the

vulnerability, the patching process may be complex and involve many

parties. For example, Google develops updates to fix security

vulnerabilities in the Android OS, but it is up to device manufacturers

to produce a device-specific update incorporating the vulnerability fix,

which can take time if there are proprietary modifications to the device's

software. Once a manufacturer produces an update, it is up to each

carrier to test it and transmit the updates to users' devices. However,

carriers can be delayed in providing the updates because they need time

to test whether they interfere with other aspects of the device or the

software installed on it.

In addition, mobile devices that are older than two years may not

receive security updates because manufacturers may no longer support

these devices. Many manufacturers stop supporting smart-phones as

soon as 12 to 18 months after their release. Such devices may face

increased risk if manufacturers do not develop patches for newly

discovered vulnerabilities.

1.7 Software on mobile devices may be out-of-date. Security patches for

third-party applications are not always developed and released in a

timely manner. In addition, mobile third-party applications, including

web browsers, do not always notify users when updates are available.

Unlike traditional web browsers, mobile browsers rarely get updates.


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Using outdated software increases the risk that an attacker may exploit

vulnerabilities associated with these devices.

1.8 Mobile devices often do not limit Internet connections. Many mobile

devices do not have firewalls to limit connections. When the device is

connected to a wide area network it uses communications ports to

connect with other devices and the Internet. A hacker could access the

mobile device through a port that is not secured. A firewall secures

these ports and allows the user to choose what connections he wants to

allow into the mobile device. Without a firewall, the mobile device may

be open to intrusion through an unsecured communications port, and

an intruder may be able to obtain sensitive information on the device

and misuse it.

1.9 Mobile devices may have unauthorized modifications. The process of

modifying a mobile device to remove its limitations so users can add

features (known as "jail-breaking" or "rooting") changes how security for

the device is managed and could increase security risks. Jail-breaking

allows users to gain access to the operating system of a device so as to

permit the installation of unauthorized software functions and

applications and/or to not be tied to a particular wireless carrier. While

some users may jailbreak or root their mobile devices specifically to

install security enhancements such as firewalls, others may simply be

looking for a less expensive or easier way to install desirable

applications. In the latter case, users face increased security risks,

because they are bypassing the application vetting process established

by the manufacturer and thus have less protection against

inadvertently installing malware. Further, jail-broken devices may not

receive notifications of security updates from the manufacturer and

may require extra effort from the user to maintain up-to-date software.


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1.10 Communication channels may be poorly secured. Having

communication channels, such as Bluetooth communications, "open" or

in "discovery" mode (which allows the device to be seen by other

Bluetooth-enabled devices so that connections can be made) could allow

an attacker to install malware through that connection, or

surreptitiously activate a microphone or camera to eavesdrop on the

user. In addition, using unsecured public wireless Internet networks or

WiFi spots could allow an attacker to connect to the device and view

sensitive information.

2. Present method of Accessing Mobile Device using different

authentication techniques

2.1 Multi-level authentication

Multi-level authentication has been considered before. As in progressive

authentication, data and applications are categorized in different levels

of authorization, variously called “hats” [23], “usage profiles” [13],

“spheres” [21], “security levels” [4], or ”sensitive files” [25]. With the

exception of Treasure-Phone [21] and MULE [25], most of this work has

been conceptual, with no actual implementation. Treasure-Phone

divides applications into multiple access spheres and switches from one

sphere to another using the user’s location, a personal token, or

physical “actions” (e.g., locking the home door would switch from the

“Home” to the “Closed” sphere). However, these sphere switching criteria

have flaws. First, location is rather unreliable and inaccurate, and when

used in isolation, it is difficult to choose the appropriate sphere (e.g.,

being alone at home is different than being at home during a party).

Second, the concept of personal tokens requires users to carry more

devices. Third, monitoring physical “actions” assumes that the device

can sense changes in the physical infrastructure, something that is not


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yet viable. Conversely, progressive authentication enables automatic

switching among the multiple levels of authentication by relying on

higher-accuracy, simpler and more widely available multi-modal

sensory information.

MULE proposes to encrypt sensitive files stored in laptops based

on their location: if the laptop is not at work or at home, these files are

encrypted. Location information is provided by a trusted location device

that is contacted by the laptop in the process of regenerating decryption

keys. Progressive authentication protects applications, not files, and it

uses multiple authentication factors, unlike MULE, which uses location

exclusively.

2.2 Automatic authentication

Other forms of automatic authentication use a single authentication

factor such as proximity [6, 7, 12], behavioral patterns [22], and

biometrics, such as typing patterns [1,16], hand motion and button

presses [3]. Most of these techniques are limited to desktop computers,

laptops or specific devices (e.g., televisions [3]). The closest to our work

is Implicit Authentication [22], which records a user’s routine tasks

such as going to work or calling friends, and builds a profile for each

user. Whenever deviations from the profile are detected, the user is

required to explicitly authenticate. Progressive authentication differs

from this work in that it uses more sensory information to enable real-

time, finer granularity modeling of the device’s authentication state. On

the other hand, any of that proximity, behavioral and biometric patterns

could be plugged into our system. Transient authentication [6, 7]

requires the user to wear a small token and authenticate with it from

time to time. This token is used as a proximity cue to automate laptop

authentication. This approach requires the user to carry and


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authenticate with an extra token, but its proximity-based approach is

relevant to our work in that it also leverages nearby user-owned devices

(i.e., the tokens) as authentication signals.

2.3 Mobile device authentication

The design of more intuitive and less cumbersome authentication

schemes has been a popular research topic. Current approaches can be

roughly classified into knowledge-based, multi-factor, and biometric

authentication techniques. All three are orthogonal to progressive

authentication. Our goal is not to provide a new “explicit”

authentication mechanism, but instead to increase the usability of

current mechanisms by reducing the frequency at which the user must

authenticate. When explicit authentication is required, any of these

techniques can be used. Knowledge-based approaches assume that a

secret (e.g., a PIN) is shared between the user and the device, and must

be provided every time the device is used. Due to the limited size of

phone screens and on-screen keyboards, this can be a tedious process

[5], especially when it is repeated multiple times per day. In multi-factor

authentication, more than one type of evidence is required. For

instance, two-factor authentication [2,20,24] requires a PIN and secured

element such as a credit card or USB dongle. This practice presents

major usability issues, as the need to carry a token such as Secure-ID

[20] goes against the user’s desire to carry fewer devices. Biometric

schemes [5, 10, 17] leverage biometrics [11] or their combinations [8, 9],

such as face recognition and fingerprints, to authenticate the user with

high accuracy. Even though very secure, biometric identification comes

with acceptability, cost and privacy concerns [17], and is especially

cumbersome on small devices.


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3. Proposed Progressive Authentication Method accessing multiple

information in Mobile Devices

3.1 Enable user authentication: Devices can be configured to require

passwords or PINs to gain access. In addition, the password field can be

masked to prevent it from being observed, and the devices can activate

idle-time screen locking to prevent unauthorized access.

3.2 Enable two-factor authentication for sensitive transactions: Two-

factor authentication can be used when conducting sensitive

transactions on mobile devices. Two-factor authentication provides a

higher level of security than traditional passwords. Two-factor refers to

an authentication system in which users are required to authenticate

using at least two different "factors" — something you know, something

you have, or something you are — before being granted access. Mobile

devices themselves can be used as a second factor in some two-factor

authentication schemes used for remote access. The mobile device can

generate pass codes, or the codes can be sent via a text message to the

phone. Two-factor authentication may be important when sensitive

transactions occur, such as for mobile banking or conducting financial

transactions.

3.3 Verify the authenticity of downloaded applications: Procedures can

be implemented for assessing the digital signatures of downloaded

applications to ensure that they have not been tampered with.

3.4 Install antimalware capability: Antimalware protection can be

installed to protect against malicious applications, viruses, spyware,

infected secure digital cards, and malware-based attacks. In addition,

such capabilities can protect against unwanted (spam) voice messages,

text messages, and e-mail attachments.


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3.5 Install a firewall: A personal firewall can protect against

unauthorized connections by intercepting both incoming and outgoing

connection attempts and blocking or permitting them based on a list of

rules.

3.6 Install security updates: Software updates can be automatically

transferred from the manufacturer or carrier directly to a mobile device.

Procedures can be implemented to ensure these updates are

transmitted promptly.

3.7 Remotely disable lost or stolen devices: Remote disabling is a

feature for lost or stolen devices that either locks the device or

completely erases its contents remotely. Locked devices can be

unlocked subsequently by the user if they are recovered.

3.8 Enable encryption for data stored on device or memory card: File

encryption protects sensitive data stored on mobile devices and memory

cards. Devices can have built-in encryption capabilities or use

commercially available encryption tools.

3.9 Enable white-listing: White-listing is a software control that permits

only known safe applications to execute commands.

3.10 Establish a mobile device security policy: Security policies define

the rules, principles, and practices that determine how an organization

treats mobile devices, whether they are issued by the organization or

owned by individuals. Policies should cover areas such as roles and

responsibilities, infrastructure security, device security, and security

assessments. By establishing policies that address these areas,

agencies can create a framework for applying practices, tools, and

training to help support the security of wireless networks.


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3.11 Provide mobile device security training: Training employees in an

organization's mobile security policies can help to ensure that mobile

devices are configured, operated, and used in a secure and appropriate

manner.

3.12 Establish a deployment plan: Following a well-designed

deployment plan helps to ensure that security objectives are met.

3.13 Perform risk assessments: Risk analysis identifies vulnerabilities

and threats, enumerates potential attacks, assesses their likelihood of

success, and estimates the potential damage from successful attacks on

mobile devices.

3.14 Perform configuration control and management: Configuration

management ensures that mobile devices are protected against the

introduction of improper modifications before, during, and after

deployment [21].

CONCLUSION

Connecting to an unsecured Mobile devices network could let

attacker access personal information from a device, putting users at

risk for data and identity theft. One type of attack that exploits the

mobile devices network is known as man-in-the-middle, where an

attacker inserts himself in the middle of the communication stream and

steals information. Progressive authentication areas such as mutli-level

authentication systems, context-based and automatic authentication,

and mobile device primary authentication are in general methods of

accessing the mobile device. The important insight of our research is to

combine multiple authentication signals to determine the user’s level of

authenticity, and surface authentication only when this level is too low

for the content being requested. Overall in general progressive


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authentication offers a new point in the design of mobile authentication

and provides users with more options in balancing the security and

convenience of their mobile devices.

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1998), 1295–1307.

[10] Jain, A., Bolle, R., and Pankanti, S. Biometrics: Personal

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[12] Kalamandeen, A., Scannell, A., de Lara, E., Sheth, A., and LaMarca, A. Ensemble: cooperative proximity-based authentication. In

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[13] Karlson, A. K., Brush, A. B., and Schechter, S. Can I borrow your phone?: Understanding concerns when sharing mobile phones. In Proc. of CHI ’09 (2009), ACM, pp. 1647–1650.

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Mobile Phones. In Proc. of Pervasive 2011 (June 12-15 2011), pp. 188–205.

[15] Mobile wallet worked to UK shoppers. http://www.bbc.co.uk/news/technology-13457071.

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PKDD ’03 (2003), Springer, pp. 363–374.

17-[27] Prabhakar, S., Pankanti, S., and Jain, A. K. Biometric

recognition: Security and privacy concerns. IEEE Security and Privacy 1 (2003), 33–42.

[18] Priyantha, B., Lymberopoulos, D., and Liu, J. LittleRock: Enabing Energy E_cient Continuous Sensing on Moble Phones. Tech. Rep. MSR-

TR-2010-14, Microsoft Research, February 18, 2010.

[19] Priyantha, B., Lymberopoulos, D., and Liu, J. LittleRock: Enabling

Energy-E_cient Continuous Sensing on Mobile Phones. IEEE Pervasive Computing 10 (2011), 12–15.

[20] RSA SecurID. http://www.rsa.com/node.aspx?id=1156.

[21] Seifert, J., De Luca, A., Conradi, B., and Hussmann, H. TreasurePhone: Context-Sensitive User Data Protection on Mobile Phones. In Proc. of Pervasive ’10. 2010, pp. 130–137.

[22] Shi, E., Niu, Y., Jakobsson, M., and Chow, R. Implicit

authentication through learning user behavior. In Proc. of ISC ’10 (October 2010), pp. 99–113.

[23] Stajano, F. One user, many hats; and, sometimes, no hat – towards a secure yet usable PDA. In In Proc. of Security Protocols Workshop (2004).

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[25] Studer, A., and Perrig, A. Mobile user location-specific encryption (MULE): using your access as your password. In Proc. of WiSec ’10

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[26] Texas Instruments. OMAPTM 5 mobile applications platform, 13 July 2011. Produ ct Bulletin


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HYBRID VEHICLES

Abhilash A.S & Yashwanth N

([email protected]) 7th Sem Mechanical Engineering

ATME COLLEGE OF ENGINEERING MYSORE

ABSTRACT

As modern culture and technology continue to develop, the

growing presence of global warming and irreversible climate change

draws increasing amounts of concern from the world’s population.

Earth’s climate is beginning to transform, proven by the frequent severe

storms, the drastic shrinking of polar ice caps and mountain glaciers,

the increased amount of flooding in coastal areas, and longer droughts

in arid sections of the world. There are large holes in the ozone layer of

the earth’s atmosphere and smog levels are ever increasing, leading to

decreased air quality. Countries around the world are working to

drastically reduce CO2 emissions as well as other harmful

environmental pollutants.

Amongst the most notable producers of these pollutants are

automobiles, which are almost exclusively powered by internal

combustion engines and spew out unhealthy emissions. Cars and

trucks are responsible for almost 25% of CO2 emission, and other

major transportation Methods account for another 12%.In the opinion

of many, cars are a large contributor to pollutions levels and, in the

bigger picture, global warming. With immense quantities of cars on the

road today, pure combustion engines are quickly becoming a target of

global warming blame.

Internal combustion engines account for a lot of the pollution

problems, but the issue still stands as to what system will drive the



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next wave of automotive vehicles. One potential alternative to the

world’s dependence on standard combustion engine vehicles are hybrid

cars. Hybrids, like their name suggests, are vehicles that utilize multiple

forms of fuel to power their engines. In the majority of modern hybrids,

cars are powered by a Combination of traditional gasoline power and

the addition of an electric motor. In this sort of hybrid engine, the

combustion engine is used at high speeds for long distances, such as

the highway, and the electric engine at low speeds and short distances,

such as in urban areas. By incorporating alternative energy

drive‐ trains into vehicles that also use combustion engines, they allow

for a considerably cleaner mode of transportation.

Key words: Vehicles, pollution, engines, hybrid.

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Introduction:

What is a Hybrid Engine System?

A hybrid engine system is a system that uses two or more distinct

power sources to move the vehicle. It is a Fusion between an internal

combustion engine and electric motor. However other mechanisms is

used to capture and utilize different functions through different power

combinations

Automobile hybrid systems combine two motive power sources, such as

an internal combustion engine and an electric motor, to take advantage

of the benefits provided by these power sources while compensating for

each other’s shortcomings, resulting in highly efficient driving

performance. Although hybrid systems use an electric motor, they do

not require external charging, as do electric vehicles. They are also

called Hybrid Electric Vehicles (HEV’s).

Hybrid System Configurations

The following three major types of hybrid systems are being used in the

hybrid vehicles currently on the market:

1) SERIES HYBRID SYSTEM

The engine drives a generator, and an electric motor uses this generated

electricity to drive the wheels. This is called a series hybrid system

because the power flows to the wheels in series, i.e., the engine power

and the motor power are in series. A series hybrid system can run a

small- output engine in the efficient operating region relatively steadily,

generate and supply electricity to the electric motor and efficiently

charge the battery. It has two motors—a generator (which has the same


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structure as an electric motor) and an electric motor. This system is

being used in the Coaster Hybrid.

2) PARALLEL HYBRID SYSTEM

In a parallel hybrid system, both the engine and the electric motor drive

the wheels, and the drive power from these two sources can be utilized

according to the prevailing conditions. This is called a parallel hybrid

system because the power flows to the wheels in parallel. In this

system, the battery is charged by switching the electric motor to act as

a generator, and the electricity from the battery is used to drive the

wheels. Although it has a simple structure, the parallel hybrid system

cannot drive the wheels from the electric motor while simultaneously

charging the battery since the system has only one motor.

3) SERIES/PARALLEL HYBRID SYSTEM

This system combines the series hybrid system with the parallel hybrid

system in order to maximize the benefits of both systems. It has two

motors, and depending on the driving conditions, uses only the electric

motor or the driving power from both the electric motor and the engine,

in order to achieve the highest efficiency level. Furthermore, when

necessary, the system drives the wheels while simultaneously

generating electricity using a generator. This is the system used in the

Prius and the Estima Hybrid.

Characteristics of Hybrid Systems

Hybrid systems possess the following four characteristics:

1. ENERGY-LOSS REDUCTION


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The system automatically shuts off the engine when the vehicle comes

to a stop and restarts it when the accelerator is pressed. This prevents

wasted energy from idling.

2. ENERGY RECOVERY AND REUSE

The energy that would normally be wasted as heat during deceleration

and braking is recovered as electrical energy, which is then used to power the

starter and the electric motor.

3. MOTOR ASSIST

The electric motor assists the engine in accelerating, passing, or hill

climbing. This allows a smaller, more efficient engine to be used. In

some vehicles, the motor alone provides power for low-speed driving

conditions where internal combustion engines are least efficient.

9. HIGH-EFFICIENCY OPERATION CONTROL

The system maximizes the vehicle’s overall efficiency by using the

electric motor to run the vehicle under operating conditions in

which the engine’s efficiency is low and by generating electricity

under operating conditions in which the engine’s efficiency is

high. The series/parallel hybrid system has all of these

characteristics and therefore provides both superior fuel efficiency

and driving performance.

10. REGENERATIVE BRAKING

The system uses a process called regenerative braking to store the

kinetic energy generated by brake use in the batteries, which in

turn will power the electric motor. The electric motor applies


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resistance to the drive train causing the wheels to slow down. In

return, the energy from the wheels turns the motor, which

functions as a generator, converting energy normally wasted

during coasting and braking into electricity, which is stored in a

battery until needed by the electric motor.

11. IMPROVED AERODYNAMIC

Improving aerodynamics; (part of the reason that SUVs get such

bad fuel economy is the drag on the car. A box shaped car or

truck has to exert more force to move through the air causing

more stress on the engine making it work harder). Improving the

shape and aerodynamics of a car is a good way to help better the

fuel economy and also improve handling at the same time.

12. OTHERS

Using low rolling resistance tires (tires were often made to give a quiet,

smooth ride, high grip, etc., but efficiency was a lower priority). Tires

cause mechanical drag, once again making the engine work harder,

consuming more fuel. Hybrid cars may use special tires that are more

inflated than regular tires and stiffer or by choice of carcass structure

and rubber compound have lower rolling resistance while retaining

acceptable grip, and so improving fuel economy whatever the power

source.

Powering the a/c, power steering, and other auxiliary pumps electrically

as and when needed; this reduces mechanical losses when compared

with driving them continuously with traditional engine belts.

These features make a hybrid vehicle particularly efficient for city traffic

where there are frequent stops, coasting and idling periods. In addition

http://en.wikipedia.org/wiki/Automotive_aerodynamics


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noise emissions are reduced, particularly at idling and low operating

speeds, in comparison to conventional engine vehicles.

Benefits of Hybrid Cars: There are many great benefits of hybrid cars.

1. Built with lightweight materials, these cars are very compact in size.

The engine is built to be very fuel efficient. When the vehicle stops at a

traffic light, the engine with automatically turn off and restart whenever

the car if put into a gear.

2. These cars have the benefit of being run by a gasoline engine and an

electric motor which exists for acceleration.

3. The batteries of the electric motor get recharged themselves by

utilizing the kinetic energy generated during braking.

4. Hybrid vehicle engines generate fewer emissions, provide good

mileage, idle less, and are very fuel efficient. These hybrid vehicles can

help save planet.

5. The aerodynamic architecture lessens drag and the tires are built

with a unique rubber which lessens fiction.

6. The battery which is inbuilt has huge competence and is composed of

nickel-metal-hydride.

7. The power-train equipment permits utilization of a couple of power

sources and improves mileage.

8. There are numerous options to choose from. Honda, Ford, Toyota,

GMC, and Chevrolet are a few worth mentioning.

http://en.wikipedia.org/wiki/Roadway_noise


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9. In case you select a hybrid vehicle then the US Government will

appreciate your selection by providing you considerable tax breaks.

10. Driving a hybrid implies that you are dynamic in guaranteeing the

environment is clean and that you care for your planet. It also indicates

that you are a responsible citizen who wants to save fuel which is

valuable.


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ANTI-LOCK BREAKING SYSTEM

Dhanush N.S & Sachin Pande M.P

([email protected]) 7th Sem Mechanical Engineering

ATME COLLEGE OF ENGINEERING MYSORE

ABSTRACT

Motor driving is a skill which requires extensive practice. Much

has been told, presented on happenings of accidents due to errors in

driving. It is even fascinated to indicate that while driving we move into

different emotions based on the path, mode and various factors which

will be involved in driving.

The drawback of any motor driving leads to an accident, which

happens in a fraction of a second. The mental presence of the driver

during the entire process of driving itself is an expertise a driver has to

have. The much more complicated concept is we have to drive not only

for ourselves but we may have to map the driving skills of other drivers

who are driving other vehicles along.

The type, class, speed and other features of various vehicles will

bring different complexities for driving.

All these complexities can be reduced to some extent by the

concept of self locking and unlocking of wheels when heavy break is

applied. This concept helps the driver not only control the vehicle but

also to steer the vehicle to safety.

The Anti-Lock BRAKE SYSTEM (ABS) is a mechatronic system

where mechanical braking, hydraulic system and the electronic sensor

system work in tandem to prevent wheel locking during heavy braking.

This allows the driver to maintain steering control while stopping

vehicle in shortest distance possible. Since ABS will not allow the tire to


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stop rotating, one can brake and steer at the same time. The braking

and steering ability of the vehicle is limited by the amount of traction

the tire can generate.

This paper mainly focuses on the functioning, types and

applications of Anti-lock breaking system.

Key words: vehicle, speed, breaking, self unlocking.

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INTRODUCTION

In a little over 100 years since automobiles took hold of people’s

imagination technologies were designed to accelerate them faster and

reach higher speeds. Car manufacturers worldwide are vying with each

other to invent more reliable gadgets, there by coming closer to the

dream of the ‘Advanced safety vehicle’ or ‘Ultimate safety vehicle’, on

which research and development has been going on for the past several

year. The most recent advancement in braking system being the Anti-

lock breaking system (ABS). Wheel lockup during braking causes

skidding which in turn cause a loss of traction and vehicle control. This

reduces the steering ability to change direction. So the car slides out of

control. But the road wheel that is still rotating can be steered. That is

what ABS is all about. With such a system, the driver can brake hard,

take the evasive action and still be in control of the vehicle in any road

condition at any speed and under any load. ABS does not reduce

stopping distance, but compensates the changing traction or tyre

loading by preventing wheel lockup.

CONCEPT OF ABS

The theory behind anti-lock brakes is simple. A skidding wheel has less

traction than a non-skidding wheel. If the vehicles have been stuck on

ice and if the wheels are spinning then the vehicle has no traction, this

is because the contact patch is sliding relative to the ice. Good drivers

have always pumped the brake pedal during panic stops to avoid wheel

lock up and the loss of steering control. ABS simply gets the pumping

job done much faster and in much precise manner than the fastest

human foot.


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By keeping the wheels from skidding while you slow down, anti-lock

brakes have two benefits. You'll stop faster and you’ll be able to steer

while you stop.

ABS COMPONENTS

Many different ABS are found on today’s vehicles. These designs are

varied by their basic layout, operation and components. The ABS

components can be divided mainly into four components

Speed sensors

Valves

Pump

Controller

Speed sensors

The anti-lock braking system needs some way of knowing when a wheel

is about to lock up. The speed sensors, which are located at each wheel,

or in some cases in the differential, provide this information.

Valves

There is a valve in the brake line of each brake controlled by the ABS.

On some systems, the valve function in three positions-

In position one, the valve is open; pressure from the

master cylinder is passed right through to the brake.

In position two, the valve blocks the line, isolating

that brake from the master cylinder. This prevents

the pressure from rising further should the driver

push the brake pedal harder.

In position three, the valve releases some of the

pressure from the brake.


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Pump

When the ABS system operates the brake lines lose pressure. The pump

re-pressurizes the system.

Controller

The controller is an electrical control type unit in the car which receives

information from each individual wheel speed sensor, in turn if a wheel

loses traction the signal is sent to the controller, the controller will then

limit the brake force (EBD) and activate the ABS modulator which

actuates the braking valves on and off.

OPERATION

Typically ABS includes a central electronic control unit (ECU),

four wheel speed sensors, and at least two hydraulic valves within the

brake hydraulics. The ECU constantly monitors the speed of each

wheel, if it detects a wheel rotating significantly slower than the others,

a condition indicative of impending wheel lock, it actuates the valves to

reduce hydraulic pressure to the brake at the affected wheel, thus

reducing the braking force on that wheel, the wheel then turns faster.

Conversely, if the ECU detects a wheel turning significantly faster than

the others it actuates the valves to increase the hydraulic pressure to

the brake at the affected wheel so the braking force is reapplied, slowing

down the wheel. This process is repeated continuously and can be

detected by the driver via brake pedal pulsation. Some anti-lock

systems can apply or release braking pressure 15 times per

second. Because of this, the wheels of cars equipped with ABS are

practically impossible to lock even during panic braking in extreme

conditions.


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Modern ABS applies individual brake pressure to all four wheels

through a control system of hub-mounted sensors and a

dedicated micro-controller. ABS is offered or comes standard on most

road vehicles produced today and is the foundation for electronic

stability control systems, which are rapidly increasing in popularity.

TYPES OF ANTILOCK BRAKE SYSTEMS

Four channel, four sensors ABS

This is the best scheme. There is speed sensor on all four wheels and a

separate valve for all the four wheels. With this set up the controller

monitors each wheel individually to make sure it is achieving maximum

braking force.

Three-channel, four-sensor ABS

There is a speed sensor on all four wheels and a separate valve for each

of the front wheels, but only one valve for both of the rear wheels.

Three channel, three sensor ABS

This scheme is commonly found on pickup trucks with four wheels

ABS, has a speed sensor and a valve for each of the front wheels, with

one valve and one sensor for both rear wheels. The speed sensor for the

rear wheel is located in the rear axle.

The rear wheels are monitored together, they both have to start to lock

up before the ABS is activated on the rear wheels. With this system, it is

possible that one of the rear wheels will lock during a stop, reducing

brake effectiveness.

One channel, one sensor abs


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This scheme is commonly found on pickup trucks with rear wheel ABS.

it has one valve, which controls both rear wheels, and one speed sensor,

located in the rear axle. This system operates the same as the rear end

of the rear channel system. The rear wheels are monitored together and

both have to start to lock up before the abs kicks in. in this system is

also possible that one of the rear wheels will lock reducing brake

effectiveness.

ADVANTAGES OF ABS

It allows the driver to maintain directional

stability and control over steering during

braking

Safe and effective

Automatically changes the brake fluid pressure

at each wheel to maintain optimum brake

performance.

ABS absorbs the unwanted turbulence shock

waves and modulates the pulses thus

permitting the wheel to continue turning under

maximum braking pressure.

DISADVANTAGES OF ABS

It is very costly


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Maintenance cost of a car equipped with ABS is more.

SURVEY

A June 1999 National Highway Traffic Safety Administration (NHTSA)

study found that ABS increased stopping distances on loose gravel by

an average of 27.2 percent.

The Insurance Institute for Highway Safety released a study in 2010

that found motorcycles with ABS 37% less likely to be involved in a fatal

crash than models without ABS.

A 2004 Australian study by Monash University Accident

Research Centre found that ABS.

Reduced the risk of multiple vehicle crashes by 18

percent,

Increased the risk of run-off-road crashes by 35

percent.

CONCLUSION

ABS has been so far developed to a system, which provides rapid,

automatic braking in response to signs of incipient wheel locking by

alternatively increasing and decreasing hydraulic pressure in the brake

line Statistics show that approximately 40 % of automobile accidents

are due to skidding. In real world conditions, even an alert and

experienced driver without ABS would find it difficult to match or

improve on the performance of a typical driver with a modern ABS-

equipped vehicle. ABS reduces chances of crashing, and/or the severity

of impact. In gravel, sand and deep snow, ABS tends to increase

braking distances. On these surfaces, locked wheels dig in and stop the

http://en.wikipedia.org/wiki/National_Highway_Traffic_Safety_Administration

http://en.wikipedia.org/wiki/Insurance_Institute_for_Highway_Safety

http://en.wikipedia.org/wiki/Multiple-vehicle_collision

http://en.wikipedia.org/wiki/Run-off-road_collision


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vehicle more quickly. ABS prevents this from occurring. If there is an

ABS failure, the system will revert to normal brake operation. Normally

the ABS warning light will turn on and let the driver know there is a

fault.


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GET THE PNR STATUS FROM

RAILWAY TICKET

Mr. Raghunatha.B 1, Dr. Y.H.Sharath Kumar 2 1Research Scholar, University Of Mysore, India

Abstract

In this project, we have designed the system which provides the PNR

status in the Audio/Visual form. We have also created the railway ticket

database for the experimentation purpose. In the hierarchy of the

proposed system, the railway ticket images are first localized and the

segmented. After segmentation, the numerals are obtained using

Template based matching technique. Then the Text To Speech (TTS) tool

is used to convert the text PNR status to audio information. The

performance of the proposed PNR status system is validated by the

accuracy measure.

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1. INTRODUCTION

Number recognition ability to recognize numbers out of order and

to understand how numbers relate to objects, without number

recognition, addition and subtraction are impossible. Students need

plenty of opportunities to practice those skills in many different ways.

Number recognition is classified into two types as off-line and on-line

recognition methods. In the off-line recognition, the writing is usually

captured optically by a scanner and the completed writing is available

as an image. But, in the on-line system, the two dimensional

coordinates of successive points are represented as a function of time

and the order of strokes made by the writer are also available. The

online methods have been shown to be superior to their off-line counter

parts in recognizing characters due to the temporal information

available with the former. However, in the off-line systems, the pattern

recognition has been successfully used to yield comparably high

recognition accuracy levels. Several applications including mail sorting,

bank processing, document reading and postal address recognition

require number recognition systems.

Fig 1.1 Railway ticket status system

2. Passenger name record (PNR)

Passenger Name Record (PNR) is a record in the database of a Computer

reservation system (CRS) that contains the group of passengers

travelling together Passenger name record is called PNR having 10 digit


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numbers in Indian railway passenger’s reservation system. These

records include: PNR Number ,name of passenger, age, gender, number

of passenger ,telephone number ,address, train number ,class of travel,

coach number seat /berth number and status of ticket is confirmed.

Fig1.2 Sample railway tickets

3. Motivation

The PNR Status tool checks the PNR number for any Train in India to

confirm our railway reservation PNR status. Checking the Ticket Status

is easy but many times people got stuck because they do not

understand the meaning of it.

4. Applications

This project useful for blind people. We can also get easily PNR status

from this system. It is used for illiterate people. It also an aid for old age

people

5. Proposed segmentation module


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Fig 1.4 proposed segmented module

6. Challenges of number recognition

Project can face challenges

Position irregularity of PNR number on railway ticket.

Fig 1.5 Sample PNR cropped image

7. Original image and segmented output image

Fig 1.6 Original image Fig 1.7 Segmented output image

8. Number dataset


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Fig 1.3 Sample number dataset

9. Template matching:

Template matching is a technique in Digital image processing for

finding small parts of an image which match a template image. It can be

used in manufacturing as a part of quality control, a way to navigate a

mobile robot, or as a way to detect edges in images.

Fig 1.8 Template matching

10. Web Technology

JavaScript: JavaScript an interpreted computer programming language

it was originally implemented as part of web browsers so that client-side

scripts could interact with the user, control the browser communicate

asynchronously

HyperTextMarkupLanguage (HTML):It is mark-up language for

creating Web pages and other information that can be displayed in

a web browser.HTML is written in the form of HTML element consisting

of tags enclosed in angle brackets (like <html>),

REFERENCES


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• Lebanese University, Institute of Technology, P.O.B. 813 – SaiLEBANON

• Eun Ryung Lee, Pyeoung Kee Kim. Automatic recognition of a car

license plate using color image processing [J]. Journal of Korea Institute of Telemetric and Electronics, 1995, 24(2): 128-131.

• LIH,etal A contour-based approach to multisensory image registration [J]. IEEE Transaction on Image Processing, March, 1995, 4(3): 320 – 334.

• Smith SM, Brady JM. Susan – a new approach to low level image

processing [J]. Journal of Computer Vision, 1997, 23(1): 45 – 78.

• Institute of Applied Mathematics, UCO, BP10808 – 49008 Anger

FRANCE[1] F.Ahmed and A.A.S. Away., 1993. An Adaptive Opt-electronic Neural Network for Associative Pattern Retrieval. Journal of Parallel and Distributed Computing, 17(3), pp. 245-

250. J. Swartz, 1999. “Growing ‘Magic’ of Automatic Identification”,IEEERobotics & Automation Magazine, 6(1), pp.

20-23.

• Park etal, 2000. “OCR in a Hierarchical Feature Space”, IEEE

Transactions on Pattern Analysis and Machine Intelligence, 22(4), pp. 400-407


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CBIR Based Matching and Retrieval of Drug Pill Images

Chinmaya T M

Asst. Prof. Govt College, Mandya

Abstract Automatic illicit drug pill matching and retrieval is becoming an

important problem due to an increase in the number of tablet type illicit

drugs being circulated in our society. Here we propose an automatic

method to match drug pill images based on the imprints appearing on

the tablet. This will help identify the source and manufacturer of the

illicit drugs. The feature vector extracted from tablet images is based on

edge localization and invariant moments. Instead of storing a single

template for each pill type, we generate multiple templates during the

edge detection process. The difficulties during matching due to

variations in illumination and viewpoint.

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Introduction

Illicit drugs, widely circulated in the international market, are one of the major factors influencing criminal activities. They also lead to additional

enforcement and tracking expense for law enforcement units. Drug trafficking is also one of the major factors behind violent and other illegal activities. Illicit drug makers use imprints, color, and shape to

identify the chemical substances and their quantities in each pill. Special imprints on the pills are also used for advertisement purposes.

This information includes chemical and physical description, where the physical description includes shape, color, imprint, etc…It is important to develop an image based matching tool to automatically identify illicit

drug pills based on their imprint, size, shape, color, etc. The keywords are based on the size, shape, and color of the pill (e.g., round, diamond, Rectangle, Oval etc.), but they do not utilize the imprint. Keyword-based

retrieval has a number of known limitations, namely keywords are subjective and do not capture all the information about the pill for

accurate retrieval. To develop a successful automatic pill image matching system, it is important to compensate for the variations in the appearance of the pills, due, for instance, to changes in viewpoint,

illumination or occlusion. For this reason, we utilize the gradient magnitude information to characterize the imprint patterns on the drug

pill images. Gradient magnitude is more stable than color or gray scale especially against illumination variations. Given the gradient magnitude image, Scale Invariant Feature Transform (SIFT) descriptor and Multi-

scale Local Binary Pattern (MLBP) descriptors are used to generate feature 2 vectors. In addition, invariant moment features proposed by Hu (1962) and color histogram are used to generate shape and color

feature vectors, respectively.

Related Work Tao and Grosky (1998) both had developed the how to match the Image Matching of OBIR System with Feature Point Histograms, the

traditional database approach of modeling the real world is based on manual Annotations of its salient features in terms of alphanumeric data. However, all such Annotations are inherently subjective. In some

cases, it is rather difficult to characterize certain important real-world concepts, entities, and attributes by means of text only. Shape and

spatial constraints are important data in many applications, ranging from complex space exploration and satellite information management to medical research and entertainment.

In order to overcome these problems, several schemes for data modeling

and image representation have been proposed. Once a measure of


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similarity is determined, the corresponding actual images are retrieved from the database. Due to the lack of any unified framework for image

representation, storage, and retrieval, these symbolic representation schemes and retrieval techniques have greatly facilitated image

management. The OBIR system is written primarily in Java. Windows and menus of the system provide user-friendly interfaces. A brief description of each of the system components is as follows.

This component of our system allows users to index a URL-

referenced image. The actual image is not stored in the database. In general, those image features which characterize image object shapes and spatial relations of multiple image objects can be represented as a

set of points. These Points can be tagged with labels to capture any necessary semantics. For example, a corner point of an image region has a precise location and can be labeled with the region’s identifier and

a color histogram of an image region can be represented by a point placed at the center-of-mass of the region and labeled by the histogram.

We call each of these individual 6 points representing shape and spatial features of image objects a feature points. Corner points, which are generally high-curvature points located along the crossings of image

object’s edges or boundaries, will serve as the feature points for our experiment. We have argued for representing an image object by the

collection of its corner points in, which proposed a quadtree-based technique for indexing such collections. SUSAN (Smallest Univalue Segment Assimilation Nucleus) is used for our corner point detection,

because SUSAN provides better results than traditional corner detection algorithms under varying levels of image brightness. OBIR is a generic object-based image retrieval system that works in a web-based

environment. Using its modular design, we may plug-in various image representation schemes as well as indexing methodologies. In this

paper, we have introduced OBIR and have demonstrated the efficacy of our symbolic image representation scheme on a small database of images. This image representation scheme crucially depends on the

quality of the technique used to find corner points. This is the weak link in our approach, but even so, we have shown that our system works well in certain environments. To refine and extend the OBIR system, we

intend to use better image processing algorithms to extract more precise image feature points. In this problem we have use various nearest

neighbor approaches to directly access relevant images. We also intend to extend our approach to work with video indexing [1].

Singha.M and Hemachandran.K (Feb 2012) both had work in this paper. We propose an image retrieval system, called Wavelet-Based

Color Histogram Image Retrieval (WBCHIR), based on the combination


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of color and texture features. The color histogram for color feature and wavelet representation for texture and location information of an image.

This reduces the processing time for retrieval of an image with more promising representatives. The extraction of color features from digital

images depends on an understanding of the theory of color and the representation of color in digital images. The distance formulas used by many researchers, for image retrieval, include Histogram Euclidean

Distance, Histogram Intersection Distance, Texture is also considered as one of the feature extraction attributes by many researchers.

Although there is no formal definition for texture, intuitively this descriptor provides measures of the properties such as smoothness and regularity. Mainly the texture features of an image are analyzed through

statistical, structural and spectral methods. In this paper it extracts some features like color feature The color feature has widely been used in CBIR systems, because of its easy and fast Computation The

extraction of color features from digital images depends on an understanding 7 of the theory of color and the representation of color in

digital images. The color histogram is one of the most commonly used color feature representation in image retrieval. A color histogram represents the distribution of colors in an image, through a set of bins,

where each histogram bin corresponds to a color in the quantized color space. A color histogram for a given image is represented by a vector:

H = {H [0], H [1], H [2], H [3] …...…H [i]… H [n]} Where ‘ i ’ is the color bin in the color histogram and H [i] represents the

number of pixels of color ‘i’ in the image, and n is the total number of bins used in color histogram. Typically, each pixel in an image will be assigned to a bin of a color histogram. Accordingly in the color

histogram of an image, the value of each bin gives the number of pixels that has the same corresponding color. In order to compare images of

different sizes, color histograms should be normalized. The normalized color histogram H is given as:

H = {H[0], H[1], H[2],……, H[i],…. H[n] Where Hi=Hi/p, p is the total number of pixels of an image. Like color, the texture is a powerful low-level feature for image search

and retrieval applications. Much work has been done on texture analysis, classification, and segmentation for the last four decade, still

there is a lot of potential for the research. “Texture is an attribute representing the spatial arrangement of the grey levels of the pixels in a region or image”. The common known texture descriptors are Wavelet

Transform, Gabor-filter, co-occurrence matrices he proposed method has been implemented using Matlab 7.3 and tested on a general-

purpose WANG database containing 1,000 images photo, in JPEG


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format of size 384x256 and 256x386. The search is usually based on similarity rather than the exact match. We have followed the image

retrieval technique, on different quantization schemes. In this paper, we presented a novel approach for Content Based Image Retrieval by

combining the color and texture features called Wavelet-Based Color Histogram Image Retrieval (WBCHIR).Similarity between the images is as certained by means of a distance function. The experimental result

shows that the proposed method outperforms the other retrieval methods in terms of Average Precision [2]. Several researchers have

proposed drug identification systems using content based image retrieval (CBIR).CBIR is a popular technology of image recognition which extracts physical features such as color or shape to describe an

image of the object (received Jan 2011,revised Aug 2011). These features are then used for Drug recognition. In this paper we had proposed an 8 Automated Drug Image Identification System (ADIIS),

using content based image retrieval to extract the features of drug images, and using neural networks perform drug recognition. The

features used to recognize drugs include colors, shapes, ratios, magnitudes and textures. The query image is matched with database images of drugs by the weighted Euclidean distance to calculate

similarity distance. The system then retrieves ten of the images most similar to the target drug image, allowing the user to correctly identify

the drug and obtain information about it. The major contributions and advantages of this paper are to construct an Automated Drug Image Identification System based on five features and dynamic weights to

identify drugs and improve the recognition accuracy of drugs even they are white circular drugs. The term Content Based image retrieval (CBIR), also known as query by image content ,is the application of

computer vision techniques to the image retrieval problem of searching for digital images in large databases, the term is also used to describe

the procedures necessary to retrieve images from a large collection, based on the syntactic image features. Though current CBIR System typically use low level features such as texture, color and shape,

systems that use high level features such as texture are common. Using CBIR, we extract different image processing techniques to extract the features of drugs to query a drug database.

Lin et al proposed a tablet drug image retrieval system to raise the drug

recognition of white tablets. Lin’s system extracts features including the shape, color and size. However, Lin’s method is not effective in identifying drugs, because many drugs are similar, and have the same

size and color. This system still cannot effectively extract the representative features of drugs. Xie et al, captured drug features that

users select for system identification, such as drug size, shape, weight


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and color. Xie’s system provides a way for the user to select the features extracted. Because of their popularity, white circular drugs and their

features are hard to represent, so the system cannot recognize the specific appearance of the drugs. of the intensity. Drug colors are

divided into the following ten colors: white, gray, black, purple, blue, green, orange, red and cyan. The images of RGB values were converted into HSV values.

In shape feature canny edge algorithm is used to define the edge of the

drug images and convert them into binary images. The edges is then divided into four equal blocks. An Edge Histogram Descriptor is used for the distribution edge of drug images. Five types of drugs were used to

indicate the various shapes of possible edges. In Linear Gabor the filter responses that result from the application of a

filter bank of Gabor filters can be used directly as texture features, though none of the approaches described in the literature employs such

texture features. In this study, linear Gabor features are used only for comparison. In our experiments we used two filter banks, one with symmetric and one with antisymmetric Gabor filters. This choice is

motivated by the properties of simple cells in the visual cortex which can be modeled by the Gabor filter. The spatial frequency bandwidth

and the spatial aspect ratio determine the orientation bandwidth of the filter which is about half response and is also constant for all filters in the bank used. Three different preferred spatial frequencies and eight

different preferred orientations were used, resulting in a bank of 24 Gabor filters. The application of such a filter bank results in a 24-dimensional feature vector in each point of the image, i.e. a 24-

dimensional vector field for the whole image. In Threshold Gabor features it states that, In contrast to the linear features described

above, most Gabor filter related texture features are obtained by apply ing non-linear post-11

Application

Illicit drug makers use imprints, color & shape to identify the

chemical substances and their quantities in each pill.

Special imprints on the pills are also used for advertisements purposes.

Challenges

Large intra class variations and less inter class variations: As the

pills of different class will have same color or shape or textures


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there will be large intra class variations, makes the problem difficult.

Identification of appropriate features: As pill images of different have same color, shape and texture identification of feature is a

more challenging task.

Selection of suitable classifier model is another challenging task.

Objectives

Study of suitable feature extraction based on color, shape and

texture of pill images 4

Study of fusion techniques for better representation of pill images

Study of classifiers for effective classification

Creation of large database on pill images will complete

information Motivation

In general for several peoples in society they don’t know the correct information about the correct drug for a particular disease, and in

current market there are several types of illicit drugs are available now. So to find out the correct information about the particular drug for a particular disease it’s a challenging part.

It is important that image retrieval does not solve the general image understanding problems. The retrieval system presents similar images.

The user should define what the similarity between images has to be. For example, segmentation and complete feature may not be necessary

for image similarity. So, when we want to develop an efficient CBIR, some problems have to be solved. The first problem is to select the image features that will represent the image. Naturally, images stored

with information and features that can be used for image retrieval. Some of the features can be (color, texture, shape) and some can be the

human description of the image like impressions. The second problem is the system steps to extract features of an image and to deal with large image databases. We have to keep in our mind that large image

databases are used for testing and retrieving. So this motivated to build the correct drug identification system.

Dataset samples

Here there are some different types of dataset samples are there with their suitable molecules are shown below. 30


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Conclusion This chapter we extracted features namely Shape features, Gabor

features, EDH features, LBP features and LBPV features and applied KNN classifier and noted all accuracy results. And finally get best

accuracy result as shown in the table format. Future work

In future we are going to study combination of different features of color, shape and texture.

Also study on classifier combination shall be made.

Selection of appropriate feature will be studied and designed.

An application based on the work will be carried out and will be designed in future.

References 1. Y.tao & W.I.Grosky computer science dept Wayne State University

Detroit, Michigan 48202 U.S.A.email:[email protected] & Grosky@ cs.wayne.edu.

2. Manimala and K.Hemachandran “content based image retrieval using color and texture”, dept of C.S.Assam university, silchar, India. Pin

code-788011. email:[email protected] and [email protected].

3. RUNG-CHING, YUNG-KUAN CHAN “An automatic drug image identification system based on multiple image features and dynamic

weights”, RUNG-CHEN dept of information management chaoyang university of technology n0.168,jifeng E.rd., wufeng district, Taichung 41349 taiwan ., email:[email protected].

4. P.kurizinga,N.petkov & S.E grigore “comparison of texture feature based on GABOR institute of Groningen p.o.Box 800,9700 Av Groningen, the Netherlands., email:[email protected],[email protected]@cs.rug.nl.

5. Zhao.g, pietilkainen.M, “Dynamic texture recognition using local binary patterns with an application of facial expressions” IEEE trans.Pattern Anal. mach intell29(6),915-928(2007).

6. Avneet Kaur is research scholar and pursuing M.Tech (Electronics

and Communication Engineering) in Department of Electronics & Communication Engineering, Amritsar College of engineering and

Technology, Amritsar, Punjab, India (e-mail: [email protected]). Vijay Kumar Banga is working as


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professor and Head of the Department of Electronics & Communication Engineering, Amritsar College of Engineering and Technology, Amritsar, Punjab, India (e-mail: [email protected]).

7. “Fusion of Colour, Shape and Texture Features for Content Based Image Retrieval” Pratheep Anantharatnasamy, Kaavya Sriskandaraja,

Vahissan Nandakumar and Sampath Deegalla Department of Computer Engineering, Faculty of Engineering, University of Peradeniya, Sri Lanka [email protected], [email protected], [email protected] [email protected].

8. “Image Retrieval Based on Content Using Color Feature” Ahmed jamal afifi-jan 2,2012 44

9. Content Based Information Retrieval in Forensic Image Databases by Zeno Geradts, Department of Digital Evidence Netherlands Forensic

Institute Ministry of Justice Volmerlaan 172288 GD Rijswijk, Netherlands [email protected] December 2001.

10. Content Based Medical Image Retrieval Using Texture Features Harikrishnan.S, PG Scholar/CSE , Paavai Engineering College Yogapriya.J, Assistant Professor/CSE, Paavai Engineering College.

11. A Statistical Approach to Texture Classification from Single Images

Manik Varma and Andrew Zisserman Robotics Research Group Dept. of Engineering Science University of Oxford Oxford, OX1 3PJ, UK (manik,az)@robots.ox.ac.uk.

12. Efficient computation of Gabor features J. Ilonen, J.-K. Kamarainen, H. K¨alvi¨ainen Department of Information Technology,

Lappeenranta University of Technology, P.O.Box 20, FIN-53851 Lappeenranta, Finland.

i

Proceedings of Seminar

RECENT TOOLS FOR DIMENSIONALITY REDUCTION

IN UNDERSTANDING MEDICAL DATA

22nd AUGUST 2013

CHAMARAJANAGAR, INDIA

CHIEF EDITOR

Prof. MD Pushpavathi

ASSOCIATE EDITORS

Prof. A G Shivakumar

Prof. Annapoorneswara

Dr. Shankarappa S

Dr. Prathibha S

Sri. Rajesh K M

Smt. Shubha L N

Organized by

Department of Computer Science JSS College for Women

Chamarajanagar-571313, Karnataka, India

ii

© JSS College for Women

Chamarajanagar

August 2013

ISBN NO: 978-81-928386-0-1

Published by:

Department of Computer Science

JSS College for Women

Chamarajanagara-571313

INDIA

iii

Preface

With the advancement of science and technology, automation took place in

various sectors such as banking, business, education, medicine, agriculture

etc. The major goal of any automation task is to minimize the effort,

maximize the productivity and to enhance the quality of service. In the field

of medicine, automation systems such as intelligent experts systems and

decision support systems help physicians and medical practitioners

effectively diagnose the diseases and make right decisions in treating a

patient. In order to design expert systems or decision support systems, a

huge volume of heterogeneous data of possibly high dimension need to be

gathered, pre-processed, represented, analyzed and interpreted. So, from the

automation point of view, understanding medical data and the tools for

dealing with medical data analysis is very much important for professionals

who design experts systems as well as for physicians who validate the

designed system.

The first state level seminar on Recent Tools for Dimensionality

Reduction in Understanding Medical Data is the forum for researchers to

present the state of the art works in the areas of Medical Image Processing,

Pattern Recognition, Dimensionality reduction, Data Analysis Datamining,

and other related areas.

The seminar was more related to understanding the new avenues in medical

data understanding and the tools for analyzing the heterogeneous type of

medical data (Text, Image, and Video). The objective of this seminar was to

highlight the challenges in medical data analysis and to open up the

research issues in this area of research. Since the field of Computer Science

and Engineering plays an important role in addressing the issues in medical

data analysis and the possible solutions, it is very much essential to identify

the prominent areas of research in Computer Science related to medical

data analysis and hence this seminar has provided the platform for many

budding researchers to discuss about the trust areas of research in this

domain and motivated many young students towards research in discipline

iv

of Computer Science and Engineering. Thus the seminar is an attempt to

provide a greater insight into the latest research works in the field of medical

data and to bring together the executives of medical sector, researchers,

teachers and students for a meaningful interaction in this regard.

We are grateful to Mr. Yogeesh, dealer, Microtek UPS, Mysore. Mr.

Murugesh, Pixel Computer, chamarajanagara, Munna Furniture,

Chamarajanagar. We thank the resource persons Prof. D.S.Guru for his

valuable suggestions and guidance in organizing this seminar. We also

thank Dr. H.S. Nagendraswamy, Dr Vinay, and Dr. S Manjunath for having

delivered more resourceful lectures on comprehensive review of

understanding medical data, medical image processing challenges and

dimensionality reduction techniques respectively. We also extend our

sincere thanks to Dr. Raju, District Leprosy Officer, and Chamarajanagar….

The interest shown by the researchers in presenting the papers is highly

appreciated. Among 30 papers presented at seminar, 15 papers were

finalized by the review committee for publication. We had papers on image

retrieval, medicinal plant recognition, neurological disorders, multiple

images, video processing etc. A special thanks to our authors who have

submitted papers related to the fields of medicine, computing and image

processing all complying with the theme of the seminar.

We would like to express our gratitude to Prof. M D Pushpavathi, Principal

of the college, advisors and members of the organizing committee and staff

of JSS College for their unstinted support and encouragement in bringing

out the proceedings of the seminar. We are thankful to Degula Mudranalaya

for printing this proceeding.

Chamarajanagar Editors

v

From Chairperson’s desk

I am extremely happy that the Department of Computer Science of our

college has organized this seminar to enlighten teachers and students about

the fascinating link between Medical Data and Computer Science. The

seminar is significant in the context of the thrust being given to higher

education in order to promote research in Basics sciences. The objective of

the seminar is to expose young minds to the various facets of the birth and

evolution of the Universe and the great triumphs in the field of Medical data

in relation to computer science. I hope the seminar will rekindle interest in

the fundamental problems of Computer science.

The seminar has been organized with blessings and encouragement from

His Holiness Jagadguru Sri Shivarathri Deshikendra Mahaswamiji,

President, JSS Mahavidyapeetha. I am grateful to Sri B N Betkerur,

Executive Secretary, JSS Mahavidyapeetha and Prof. T D Subbanna,

Director, College Development Section, JSS Mahavidyapeetha for their

enthusiastic support.

I am extremely thankful to distinguished Professors Dr. D S Guru, Dr.

Nagendraswamy, Dr. Manjunath S, and Dr. Vinay for enriching the seminar

with their presentations. I am also thankful to Dr. Raju District Leprosy

Officer, Chamarajanagar, and Technical Committee members for their

valuable guidance and technical support.

The proceeding of the seminar is a testimony to the enthusiasm and interest

shown by the paper presenters and my special thanks to them. I would like

to place on record my sincere thanks to the Chief Editor, Dr. D S Guru,

editors, Dr. Nagendra Swamy, Dr. Manjunath S, Dr. Vinay and Sri. Rajesh K

M for their efforts in bringing out the proceedings.

I am extremely grateful to University Grants Commission and all other

sponsors for providing financial assistance for organizing the seminar.

vi

I would like to acknowledge the persistent and untiring efforts of Sri. Rajesh

K M, Organizing secretary and faculty of Department of Computer Science of

our college in organizing the seminar.

The organization of this seminar is due to the dedicated efforts of the

Advisors and Members of the Seminar committee, teaching and non-

teaching staff and students of the college. I would like to place on record my

sincere thanks to all of them.

Prof. M D Pushpavathi, Principal.

vii

SEMINAR COMMITTEE

Chief Patron

HIS HOLINESS JAGADGURU

SRI SHIVARATHRI DESHIKENDRA MAHASWAMIJI

President, JSS Mahavidyapeetha, Mysore

Advisors

Sri B N BETKERUR Executive Secretary, JSS Mahavidyapeetha, Mysore

Prof. S P MANJUNATH Deputy Secretary-I, JSS Mahavidyapeetha, Mysore

Prof. S SHIVAKUMARASWAMY Deputy Secretary-II, JSS Mahavidyapeetha, Mysore

Prof. T D SUBBANNA Director, CDS, JSS Mahavidyapeeta, Mysore

Sri. B NIRANJAN MURTHY Asst. Director, CDS, JSS Mahavidyapeeta, Mysore

*-*-*-*-*-

viii

Technical Advisory Committee

Chair

Dr. D S Guru Professor, DOS in CS, Manasagangothri, Mysore- 570006

Members

Dr. T Vasudev Prof. & HOD, DOS in CS, MIT, Mysore

Dr. Manjunath Rao L Director-DSSIT, Bangalore

Dr. H S Nagendraswamy

Asst. Prof. Dos in CS, UOM, Mysore

Dr. Rghuveer R

Asst. Prof.DOS in CS, NIE, Mysore

Dr. Bajanthri

Asst. Prof.DOS in CS, GEC, Chamarajanagar

Dr. C N Ravikumar

Prof. DOS in CS, SJCE, Mysore

Review Committee

Dr. Manjunath S Asst. Prof. DOS in CS, JSS College, Ooty Road, Mysore

Dr. Vinay

Asst. Prof. DOS in CS, JSS College, Ooty Road, Mysore

Dr. Harish

Asst. Prof. Dos in CS, SJCE, Mysore

Dr. Nagasundara

Asst. Prof.DOS in CS, IT, Mysore

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ix

Organizing Committee

Chairperson

Prof. M D Pushpavathi Principal, JSS College for Women, Chamarajanagar

Organizing Secretary

Sri. Rajesh K .M Head, Dept of Computer Science & BCA

JSS College for Women, Chamarajanagar

Members

Prof. A G Shivakumar Vice- Principal & PRO

Prof. VijayaKumar M V

Head, Dept. Zoology

Prof. Veeranna Head, Dept. Economics

Prof. Shivanna Head, Dept. History

Dr. Shankarappa S Head, Dept. Commerce

Prof. Poornima S Head, Dept. English

Dr. S Prathibha Head, Dept. Botany

Sri. Siddaraju S

Head, Dept. Chemistry

Smt. Arunashree K S Head, Dept. Mathematics

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CONTENTS Preface From Chairperson’s Desk

Seminar Committee

iii v

vii

Invited Talks

1. Understanding Medical Data an Overview Dr. HS Nagendraswamy

2. Medical Image Processing

Dr. Vinay 3. Dimensionality Reduction

Dr. S Manjunath

1

12

19

Papers Presented

1. An Enhanced Natural Scene Classification Based Image Browsing And Retrieval System Srinidhi

2. Kannada Handwritten Word Recognition in Bank Cheque: A Study Nandeesh P

3. A Review on Automation of Ayurvedic Plant Recognition Pradeepkumar N

4. A Review On Neurological Disorders Maheswara Prasad S

5. Disease Identification In Mulberry Leaves –Review Chaithra D

6. Recognition Of Image Inside Multiple Images Rajesh K M

7. Interpretation of Indian Classical Mudras: A Pattern Recognition Approach Manikanta P

8. Current Changes In Plagiarism Detection Nagaraju L J

9. A Mathematical Overview Of Vision Processing Ashwin Kumar H N

10. Taxonomy Of Multicast Routing Protocols For Mobile Ad-Hoc

Networks Jagadeeshkrishna S

11. Security Approaches On Progressive Authentication Method Accessing Multiple Information In Mobile Devices Santhoshkumar B N

12. Hybrid Vehicle Abilash A.S Yashavanth N

13. Anti Lock Breaking System Dhanush N.S , Sachin Pande M.P

14. Get The Pnr Status From Your Railway Ticket Raghunath

15. CBIR Based matching and Retrieval of Drug Pill Images Chinmaya T.M

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33

43

60

69

77

85

95

104

112

133

148

156

165

171

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M E S S A G E…

xiii

TECHNICAL SESSIONS