journal of image processing & pattern recognition progress vol 3 issue 3

September–December 2016

Journal of

Image Processing

& Pattern Recognition Progress

(JoIPPRP)

ISSN 2394-1995 (Online)

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Journal of Image Processing & Pattern Recognition Progress

ISSN: 2394-1995 (online)

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It is my privilege to present the print version of the [Volume 3 Issue 3] of our Journal of Image

Processing & Pattern Recognition Progress, 2016. The intension of JoIPPRP is to create an

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STM JOURNALS

1. Bangla Handwritten Digit and Basic Letter Recognition Using Machine Learning Techniques Juthika Majumder, Razia Sultana, Rameswar Debnath 1

2. Hybrid HMM/ANN Models for Improving Offline Handwritten Text Recognition Varidhi N.K., Savitha C.K., Prajna M.R., Ujwal U.J. 16

3. A Review of Image Watermarking on Different Methods with its ApplicationsSuniti Bhadoriya, Nirupma Tiwari 24

4. Segmentation Methods Applied on MR Medical ApplicationVandana Rajput, Nirupma Tiwari, Manoj Ramaiya 30

5. Ranking of Image Search by Click-Based Uniformity and Typicality Ashwath M., Balapradeep K.N., Savitha C.K., Ujwal U.J. 39

ContentsJournal of Image Processing & Pattern Recognition Progress

JoIPPRP (2016) 1-15 © STM Journals 2016. All Rights Reserved Page 1

Journal of Image Processing & Pattern Recognition Progress ISSN: 2394-1995(online)

Volume 3, Issue 3 www.stmjournals.com

Bangla Handwritten Digit and Basic Letter Recognition

Using Machine Learning Techniques

Juthika Majumder, Razia Sultana, Rameswar Debnath*

Computer Science and Engineering Discipline, Khulna University, Bangladesh

Abstract The usage of computer is increasing day by day in Bangladesh, so the use of Bangla is increasing in computer. Furthermore, the use of Bangla handwritten character is also increasing in many computer applications. There exist many techniques for recognition of handwritten character. From the studies we see that machine learning techniques are better techniques for recognition of handwritten character than others. A necessary prerequisite for measuring the performance of machine learning techniques is a large dataset for training. In this paper our contributions are two-fold. The first one is to develop large real dataset. We have developed a dataset containing 25,000 samples of Bangla handwritten digits and a dataset containing 10,200 samples of Bangla handwritten letters. Each sample in the databases is an image of size 1616 pixels. The second contribution is to a systematic evaluation of the performance of machine learning techniques for Bangla character recognition. In this paper, we choose mostly used machine learning techniques: one-against-one support vector machine, one-against-all support vector machine, artificial neural networks, and radial basis neural network. We apply several morphological operations on image dataset that can improve the performance of machine learning techniques for handwritten character recognition. From the experimental results we see that classifiers show better results with preprocessed data, and support vector machines give significantly better recognition results than those of radial basis neural network and artificial neural networks. From the results we see that 99.10% accuracy is obtained for digit recognition and 96.08% accuracy is obtained for letter recognition by support vector machine. Keywords: Support vector machine, artificial neural network, radial basic function network, principal component analysis, flood fill algorithm, thinning, dilation, opening, binarization

INTRODUCTION Now-a-days, handwritten character recognition systems are used in variety of practical and commercial applications such as mail sorting, bank processing, document reading and postal address recognition, etc. The application areas of handwritten character recognition are increasing day by day. Thus, an automatic and good recognition system is desired in every language. Handwritten characters are nonuniform in nature. Different writers write a particular character in different styles and sizes. Even the same writer can write the same character in different styles and sizes at different times. For multiscript writers, writing style of one script has impact on other similar script. Handwritten character recognition has been one of the most challenging research areas in the field of image processing and pattern recognition. It becomes more challenging and sometimes intractable when the number of letters in

Alphabet set is large and many of them are very similar. A lot of research has been done in handwritten character recognition and numerous techniques have been proposed. These include template matching techniques, statistical classifiers such as Bayes’ classifier, Hidden Markov Model (HMM), graph-based and automata based synaptic classifiers, and machine learning based techniques [1–13]. Among these techniques, the approaches based on HMM and support vector machine (SVM) (a machine learning technique) are popular in handwritten recognition tasks due to their high recognition rate.




Hybrid HMM/ANN Models for Improving Offline

Handwritten Text Recognition

Varidhi N.K., Savitha C.K., Prajna M.R., Ujwal U.J. Department of Computer science, KVG College of Engineering, Sullia, Karnataka, India

Abstract

In this approach, it proposes the use of hybrid Hidden Markov Model (HMM)/Artificial Neural Network (ANN) models for recognizing the unconstrained offline handwritten texts. Handwritten image normalization from a scanned image includes several steps, usually it begin with image cleaning, page skew correction, and line detection. For handwritten text line image several pre-processing steps to reduce variation in writing style are performed like slope and slant removal and character size normalization. The structural part has been modeled with Markov chains, and a Multilayer Perceptron (MLP) is used to estimate the emission probabilities. This approach presents a new technique to remove slope and slant from the handwritten text and also to normalize the size of text images with supervised learning methods. Slope correction and size normalizations are achieved by classifying the local extrema of text contours with MLP. With the help of Artificial Neural Network slant is removed in a nonuniform way. Experiments have been conducted on many offline handwritten text lines and the recognition rates are achieved. Keywords: Handwriting recognition, offline handwriting, HMM, hybrid HMM/ANN, neural networks, image normalization, multilayer perceptron

INTRODUCTION Handwriting recognition has been one of the most popular and challenging research areas in the field of image processing and pattern recognition. The field of handwriting recognition can be divided into two types, one is on-line and another one is off-line recognition. In on-line recognition the writer is connected physically to a computer via a mouse, or any touch sensitive device and his or her handwriting is recorded as a time dependent process. But in off-line mode, handwritten text is captured by means of a scanner and becomes available as image without any temporal information. And also the use of cameras for capturing handwriting is becoming increasingly popular today. Off-line recognition is considered the more difficult problem because of the lack of temporal information [1]. Offline handwritten text recognition is the most active areas of research in computer science. It is very difficult to recognize the handwritten text because of the high variability of writing styles [2]. The off-line and on-line handwritten character recognition techniques have different approaches, and they share a lot of common problems and solutions. Nowadays, in addition

to the powerful computers and more accurate electronic equipment such as cameras, scanners, and other electronic tablets, it uses modern methodologies such as Neural Networks (NNs), Hidden Markov Models (HMMs) and many more. LITERATURE SURVEY Machine simulation of human functions is a challenging research field as a result of the advent of digital computers. In the areas where it requires certain amount of intelligence, such as number crunching or chess playing, improvements are achieved. Humans still perform even the most powerful computers in relatively routine functions such as vision. Machine simulation of human reading is an intensive research area. In this overview, character recognition is used as an umbrella word, which covers all types of machine recognition of characters in various application domains [2].




A Review of Image Watermarking on Different

Methods with its Applications

Suniti Bhadoriya*, Nirupma Tiwari Department of Computer Science and Engineering,

Shri Ram College of Engineering and Management, Banmore, Madhya Pradesh, India

Abstract Watermarking is a process to hide some secret data in a cover file. Watermarking is a notion intently related to steganography. In these, both hide information in an image. The information hidden in this is in different forms like an image, song, video within the signal itself. We have studied comparative analysis of various approaches that may positive and negative of these techniques. This comparison can further be used to improvise and propose few new techniques for the same. Keywords: Applications, Techniques, Watermarking

INTRODUCTION Present day generation is witness of digital media improvements. Using phone camera to capture a photo is a simple example. The use of digital media is common in present era. Text, audio, video are other digital media example. We know an internet is the fastest medium of transferring data to any place in a world. This method can be using to every digital media types such as image, audio, video and documents. From many years researchers and developers worked in this area to gain best results [1]. These are few sections for basic information purpose: Overview and history of Image

watermarking Various types of Image watermarking

techniques

Applications and classification of watermarking

Image watermarking threats. A watermark image is shown in Figure 1.

Fig. 1: A Watermark Image.

BASIC OF WATERMARKING Digital Image Watermarking contains of two parts: 1. Watermark embedding 2. Watermark extraction




Segmentation Methods Applied on MR Medical

Application

Vandana Rajput*, Nirupma Tiwari, Manoj Ramaiya Department of Computer Science and Engineering, ShriRam College of Engineering and

Management, Gwalior, Madhya Pradesh, India

Abstract Image segmentation techniques as applied in MRI of human brain are developing by leaps and bounds. The human brain is the most imaging design of the nature. Cancer and neurological problems are resolved using MR of the brain. The automatic diagnostic system starts with medical imaging, segmentation and analysis of the findings and then the final decisions which are further used to treat the patient. In the real world certain noises are present. Segmentation is the process of separation of region of interest from the main image. It is a challenging task to de noise the image. Large member of algorithms for segmentation have been in practice. This review paper briefs the research made and published in the journals of international repute. The review is presented in the chronological order. The focus is on MR image segmentation and their result.

Keywords: Brain, MRI, Segmentation, Robust Fuzzy C-means clustering (RFCM), Image segmentation, Nonlocal, Brain tissue

INTRODUCTION

Human beings and their brain has always been the focus of the medical research. The image processing is the fast growing area dedicated to medical imaging. Magnetic resonance (MR) brain images are analyzed and ROI is segmented and further compared with data collected during machine learning. There are many problems of anatomy and neuro anatomical valuation in the normal human brains, the segmentation of brain tumor from MRI is an important process for treatment, planning, monitoring of therapy, examining effect of radiation, drug treatments and researching and making distinction between healthy brain and brain having tumor. MRI input parameters can be modified to get the image with clear contrast. The different gray levels in the output display the different cases of neuropathology. Neuroscience uses mapping of functional activation onto brain anatomy, the study of brain development and the analysis of neuroanatomical variability in normal brains requires the identification of brain structures in MRI images.

IMAGE SEGMENTATION USING

FUZZY C-MEAN ALGORITHM

TECHNIQUE Yuan et al. have used soft method to find the region of interest [1]. He uses Fuzzy C-means algorithm. In this method Fuzzy membership of each class is calculated to find out the segmentation of entire image. Fuzzy C-means (FCM) algorithm uses recursive method to achieve the above objective. It uses only the gray information of a pixel without considering the relationship with other pixels. Thus this membership determination is vulnerable to noise. In order to achieve better results the membership function is post processed. This increases the spatial constraint to get robustness against noise. The author has further discussed RFCM (Robust FCM) which gives better result as compared with FCM. In this work robust Fuzzy C-Means clustering is enhanced by appending nonlocal component. The authors have simulated the MR brain images and proved that the nonlocal Fuzzy C-Means clustering yields better for noisy images.




Ranking of Image Search by Click-Based Uniformity and

Typicality

Ashwath M*., Balapradeep K.N., Savitha C.K., Ujwal U.J.

Department of Computer Science, KVG College of Engineering, Sullia, Karnataka, India

Abstract According to the image search re-ranking, apart from the semantic gap and intent gap, which is the gap between the representation of user’s query and the real objective of the users, is forming a major issue in restricting the evolution of image retrieval. In order to reduce human effects, the method use image click-through data, which can be viewed as the implicative feedback from users, to help overcome the intention gap, and further to improve the performance of image search. The proposed method presents a novel re-ranking approach, named spectral clustering re-ranking with click-based similarity and typicality. Keywords: Image search, search re-ranking, click-through data, multifeature similarity, image typicality

INTRODUCTION Hundreds of thousands of images are uploaded to the internet with the help of online social media and by the popularity of mobile devices thus, building a satisfying image retrieval system is the challenge to improve the user search experience [1]. In order to improve search performance, image search re-ranking, which adjusts the initial ranking orders by mining visual content or leveraging some auxiliary knowledge, is proposed, and has been the focus of attention in both academia and industry in recent years [2, 3]. Most of the existing re-ranking methods utilize the visual information in an unsupervised and the passive manner to overcome the “semantic gap” (the gap between the low-level features and high-level of semantics) [4, 5]. Although multiple visual methods have been used to further mine useful visual information, they can only establish limited performance improvements [6, 7]. The contributions of this method can be summarized as follows: Present a novel image search re-ranking,

named spectral clustering re-ranking with click-based similarity and typicality (SCCST), which first use image click data to guide image similarity learning for multiple features, then conducts spectral clustering to group similar images into clusters, and obtained the re-ranking results by calculating click-based clusters

typicality and within the clusters click based image typicality in descending order [8].

Use click-through data and multiple visual modalities simultaneously to learn image similarity, and propose an innovative similarity learning algorithm, called click based multifeature similarity learning (CMSL), which conducts metric learning based on click-based triplets selection, while integrating multi-feature into a unified similarity space via multiple kernel learning [9].

Integrate click-through data with image typicality learning to mine the influence of this implicit feedback in determining the degree of image relevance to the given query, and further improve the image search performance [9].

RELATED WORK Visual Search Re-Ranking (2010)

According to the approach it proposed a novel visual re-ranking method by mining relevant visual patterns from the search results which are available from existing search engines on the Internet. There are several open problems. First, the number of search engines or online resources is still limited. It would be a promising topic to discover more search engines and sites and investigate how many

September–December 2016

Journal of

Image Processing

& Pattern Recognition Progress

(JoIPPRP)

ISSN 2394-1995 (Online)

www.stmjournals.com

STM JOURNALSScientific Technical Medical

journal of image processing & pattern recognition progress vol 3 issue 3

Education