preliminary lect - biomisabiomisa.org/wp-content/uploads/2020/02/pre-lect.pdf · machine learning...

1

Digital Image Processing

Preliminary Lect

3

Image Processing & Machine Vision

Low Level Process

Input: Image

Output: Image

Examples: Noise

removal, image

sharpening

From Image Processing to Machine Vision: low, mid and high-level processes

Image Processing

4

Example: Low Level Processing

5


Low Level Process

Input: Image

Output: Image

Examples: Noise

removal, image

sharpening

Mid Level Process

Input: Image

Output: Attributes

Examples: Object

recognition,

segmentation


Image Processing

6

Example: Mid Level Processing

Segmentation of image into regions

7


Low Level Process

Input: Image

Output: Image

Examples: Noise

removal, image

sharpening

Mid Level Process

Input: Image

Output: Attributes

Examples: Object

recognition,

segmentation

High Level Process

Input: Attributes/Image

Output: Understanding

Examples: Scene

understanding,

autonomous navigation


Image Processing Machine Vision

8

Example: High Level Processing

Object Classification

9


Low Level Process

Input: Image

Output: Image

Examples: Noise

removal, image

sharpening

Mid Level Process

Input: Image

Output: Attributes

Examples: Object

recognition,

segmentation

High Level Process

Input: Attributes/Image

Output: Understanding

Examples: Scene

understanding,

autonomous navigation


Image Processing Machine Vision

In this course

Some of this as well

Why Image Processing?• Images and video are everywhere!

Personal photo albums

Surveillance and security

Movies, news, sports

Medical and scientific images

Slide credit; L. Lazebnik

http://www.picsearch.com/

11

Problem Domain Application Input Pattern Output Class

Document Image

Analysis

Optical Character

Recognition

Document Image Characters/words

Document

Classification

Internet search Text Document Semantic categories

Document

Classification

Junk mail filtering Email Junk/Non-Junk

Multimedia retrieval Internet search Video clip Video genres

Speech Recognition Telephone directory

assistance

Speech waveform Spoken words

Natural Language

Processing

Information extraction Sentence Parts of Speech

Biometric Recognition Personal identification Face, finger print, Iris Authorized users for

access control

Medical Computer aided

diagnosis

Microscopic Image Healthy/cancerous cell

Military Automatic target

recognition

Infrared image Target type

Industrial automation Fruit sorting Images taken on

conveyor belt

Grade of quality

Bioinformatics Sequence analysis DNA sequence Known types of genes

Summary of Applications

Text Book & References:

• Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, 4rd Edition, 2018 (available from local market)

• Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing using MATLAB, 3rd Edition, 2020 (available from local

market) (available from local market)

• Class slides & selected research papers to be shared by the instructor

Course Information• Course Material

– Lectures slides, assignments (computer/written), solutions to problems, projects, and announcements will be uploaded on course web page.

http://biomisa.org/usman/digital-image-processing-sp-20.html

http://biomisa.org/usman/digital-image-processing

Course Contents

• Introduction to Image processing (Chapter – 1, 2)

• Image processing Fundamentals (Chapter-2)

• Image Enhancement (Spatial & Frequency Domain) (Chapter – 3, 4)

• Morphological operations (Chapter – 9)

• Segmentation (Chapter – 10, Online material, David Forsyth)

• Texture analysis (Chapter – 11, Online material, David Forsyth )

• Image Pyramids & Wavelets (Chapter – 07, Online material, David Forsyth )

• Image representation and description (Chapter – 11)

• Introduction to Machine Learning and Convolutional Neural Networks (Chapter – 12 & Stanford course on CNN)

Contents Breakdown1. Introduction to image processing and it fundamentals

a. Basics of image processingb. Image resolutionc. Connected component analysis

4 hrs

2. Image enhancement in spatial domaina. Intensity transformationsb. Histogram and its analysisc. Convolution and spatial filtering

5 hrs

3. Edge Detection and analysisa. Edge segmentation (magnitude and Phase Analysis)b. Hough Transform

2 hrs

4. Segmentation using thresholding and clusteringa. Global, local and adaptive thresholdingb. K-means clusteringc. Region growing and splitting based segmentation

2 hrs

5. Color image processinga. Formation of color imageb. Different color modelsc. Analysis of colored images

1.5 hrs

6. Morphological operations for binary and gray imagesa. Introduction to morphological operationsb. Morphological operation for binary images

c. Gray level morphological operations

2 hrs

Contents Breakdown7. Image enhancement in frequency domain

a. Basic concepts related to Fourier transformb. Sampling in frequency domain and introduction to DFT

c. Filtering in frequency

3 hrs

8. Texture Analysisa. Statistical descriptorsb. GLCM analysisc. Spectral descriptors

2 hrs

9. Convolution Neural Networksa. Basic concepts of CNNS related to Fourier transform

b. Calculating parameters and layer size for CNN architectures

2 hrs

10. Introduction to wavelets a. motivation of waveletsb. Wavelet decompositionc. wavelet energy

2 hrs

11. Descriptors a. Local Binary Patternb. Histogram of oriented gradients

2 hrs

12. Revision 2 hrs13. Classification Outside

study

Lab BreakdownLab 01 Installation & Introduction to Python and OpenCV

Lab 02 Basic Image Processing & Connected component analysis

Lab 03 Transformation Operations (Assignment-1: Extraction of features and their visualization)

Lab 04 Histogram Equalization and other transformations (Project Idea Presentations)

Lab 05 Spatial Filtering

Lab 06 Edge Detection

Lab 07 Open Lab-1

Lab 08 Image Segmentation (Assignment-2: Segmentation of skin Lesions)

Lab 9 Morphological Operations

Lab 10 Introduction to Machine Learning and Convolutional Neural Network

Lab 11 Image Segmentation using CNN (Assignment-3: Classification of dermoscopic images)

Lab 12 Image Classification using CNN

Lab 13 Frequency Analysis

Lab 14 Texture Based Descriptors

Lab 15 Open Lab -2 & Lab Final (Project Final Presentations)

Lab RubricsUnsatisfactory (0-3 marks) Satisfactory (4-6 marks) Good (7-8 marks) Excellent (9-10 marks)

SpecificationsWeight = 35%

Completed less than 70% of the requirements.

Completed between 70-80% of the requirements.



Coding Standards/ Time EfficiencyWeight = 20%

No name, date, or assignment/task title included

Poor use of white space (indentation, blank lines).

Disorganized and messy Poor use of variables (many

global variables, ambiguous naming).

Not delivered on time or not in correct format (disk, email, etc.)

Includes name, date, and assignment/task title.

White space makes program fairly easy to read.

Organized work. Good use of variables (few

global variables, unambiguous naming).

Delivered on time, and in correct format (disk, email, etc.)


Good use of white space. Organized work. Good use of variables (no

global variables, unambiguous naming)



Excellent use of white space. Creatively organized work. Excellent use of variables (no

global variables, unambiguous naming).


DocumentationWeight = 15%

No documentation included. Basic documentation has been completed including descriptions of all variables.

Purpose is noted for each function.

Clearly documented including descriptions of all variables.

Specific purpose is noted for each function and control structure.

Clearly and effectively documented including descriptions of all variables.

Specific purpose is noted for each function, control structure, input requirements, and output results.

Runtime/OutputWeight = 15%

Does not execute due to errors. User prompts are misleading or

non-existent. No testing has been completed.

Executes without errors. User prompts contain little

information, poor design. Some testing has been

completed.

Executes without errors. User prompts are

understandable, minimum use of symbols or spacing in output.

Thorough testing has been completed

Executes without errors excellent user prompts, good use of symbols, spacing in output.

Thorough and organized testing has been completed and output from test cases is included.

EfficiencyWeight = 15%

A difficult and inefficient solution.

A logical solution that is easy to follow but it is not the most efficient.

Solution is efficient and easy to follow (i.e. no confusing tricks).

Solution is efficient, easy to understand, and maintain.

Project RubricsTrait Unsatisfactory

(0-3)Satisfactory

(4-6)Good(7-8)

Exceptional(9-10)

Understanding of Problem 25%Problem

UnderstandingWeight = 15%

There is no knowledge of the problem depth.

The depth of the problem is very weak.

The depth of problem is understood to a sufficient

level.

The depth of the problem is exceptionally

understood.Review

Weight = 10%No effort made to cover

related work of the selected problem.

Related work covered is very sketchy.

Related work of the problem is covered

sufficiently except for the latest developments.

Related work of the problem is covered to an

excellent extent.

Presentation and Demonstration Skills 25%DemonstrationWeight = 25%

The demonstration is poor with no organization and

fails at the cross questioning session.

The demonstration is unorganized and cross

questioning session is sketchy.

Good skills while demonstrating project and fairing fairly good in cross

questioning

Excellent command of project is shown with exceptional question

answer session.Coding and Documentation 50%

SpecificationsWeight = 30%

The program is producing incorrect results.

The program produces correct results but does not display them

correctly.

The program works and produces the correct

results and displays them correctly. It also meets

most of the other specifications.

The program works and meets all of the specifications.

ReadabilityWeight = 10%

The code is poorly organized and very difficult

to read.

The code is readable only by someone who knows what it is

supposed to be doing.

The code is fairly easy to read.

The code is exceptionally well organized and very

easy to follow.Report

(Documentation)Weight = 10%

The documentation is simply comments

embedded in the code and does not help the reader

understand the code.

The documentation is simply comments embedded in the

code with some simple header comments separating routines.

The documentation consists of embedded

comment and some simple header documentation that

is somewhat useful in understanding the code.

The documentation is well written and clearly explains

what the code is accomplishing and how.

Grading PolicyCourse Assessment

Exam: 2 Sessional and 1 FinalHome work: 3 graded Assignments, 04 home tasks (non graded)Lab reports: 12-13 reports, 02 open Labs

Design reports: 1 Design report and 2 presentations based on Semester Project

Quizzes: 6-8 Quizzes

Grading:

Theory (67%) Lab (33%)

2 One Hour Tests (OHTs): 30%

Lab Work/Tasks (Every week) 40%

Open Lab 1 12%

Quizzes: 10% Open Lab 2 12%

Assignments 10% Home Tasks 06%

Final Exam 50% Final Project 30%

Course Learning Outcome

Course Learning Outcome (CLOs) PLOs

Learning

Level Assessments

CLO 1

Understanding the fundamentals and basic concepts

of image processing related to image enhancement,

filtering and segmentation etc

PLO 1 C2

Q1, Q2, A1,

OHT-1, Final

1-question

CLO 2

Performing different mathematical transformations,

histogram based operations and filtering concepts

for solving image enhancement and feature

extraction problems

PLO 2 C3

Q3, Q4, A2,

OHT2, Final 1-

question

CLO 3

Combining the concepts of image processing with

machine learning to analyze and design decision

support systems for image processing based

applications

PLO 3 C4, C6

Q6, A3, Final

1-question

CLO 4

Learning the use of Python and OpenCV to

implement basic image processing algorithms and to

solve real life and open ended problems

PLO 5 P4

Labs, Open

Labs, Project,

Presentation

CODE OF ETHICS

• All students must come to class on time (Attendance will be taken in first 5 to 10 mins)

• Students should remain attentive during class and avoid use of Mobile phone, Laptops or any gadgets

• Obedience to all laws, discipline code, rules and community norms

• Respect peers, faculty and staff through actions and speech

• Student should not be sleeping during class

• Bring writing material and books

• Class participation is encouraged

Policies• No extensions in assignment deadlines.

• Quizzes will be unannounced.

• Exams will be closed book but formula sheets will be allowed

• No Attendance and marking of lab tasks if late more than 15 min

• Never cheat.

– “Better fail NOW or else will fail somewhere LATER in life”

• Plagiarism will also have strict penalties.

Adapted from What is Plagiarism PowerPoint

http://mciu.org/~spjvweb/plagiarism.pptCourtesy Dr. Khawar

http://mciu.org/~spjvweb/plagiarism.ppt

Image Sources

• Electromagnetic (EM) band imaging

– Gamma ray band images

– X-ray band images

– Ultra violet band images

– Visual light and infra-red images

– Images based on micro waves or radio waves

• Non-EM band imaging

– Acoustic and ultrasonic images

– Electron microscopy

– Computer generated images (synthetic)

Light & EM Spectrum

• EM Waves

– A stream of mass less particles each travelling in a

wave like pattern, moving at the speed of light and

contains a certain bundle of energy

– The electromagnetic spectrum is split up in to bands

according to the energy per photonHighest Energy Lowest Energy

Light & EM Spectrum

Vis

ible

lig

ht

is j

ust

a p

arti

cula

r p

art

of

the

elec

tro

mag

net

ic s

pec

tru

m t

hat

can

be

sen

sed

by t

he

hu

man

eye

Examples: Imaging in EM bands

Spectral Band Example

Gamma-Rays Nuclear Medicine (Radioactive isotope

injected in the patient)

X-Rays Medical Diagnostic

Ultraviolet Fluorescence microscopy

Visible & Infrared Remote sensing, industrial inspection,

…

Microwave Radar

Radio Medicine – MRI

Examples: Imaging other Modalities

• Sound

– Geological Applications – Oil and Gas Exploration

– Medicine – Ultrasound Imaging

• Synthetic Images

– Computer generated

A synthetic image

Key Stages in DIP

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Object

Recognition

Image

Enhancement

Representation

& Description

Problem Domain

Colour Image

Processing

Image

Compression

Readings from Book (3rd Edn.)

• Chapter - 1

41

Acknowledgements

Statistical Pattern Recognition: A Review – A.K Jain et al., PAMI (22) 2000

Pattern Recognition and Analysis Course – A.K. Jain, MSU

Pattern Classification” by Duda et al., John Wiley & Sons.

Digital Image Processing”, Rafael C. Gonzalez & Richard E. Woods, Addison-Wesley, 2008

Machine Vision: Automated Visual Inspection and Robot Vision”, David Vernon, Prentice Hall, 1991

www.eu.aibo.com/

Advances in Human Computer Interaction, Shane Pinder, InTech, Austria, October 2008

Mat

eria

l in

th

ese

slid

es h

as b

een

tak

en f

rom

, th

e fo

llow

ing

reso

urc

es

http://www.eu.aibo.com/

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