Edge Detection Using Fuzzy Logic and Cla

Edge detection using Fuzzy Logic and Automata Theory

Title Page

By

Takkar Mohit

Supervisor

A Thesis Submitted to

In Partial Fulfillment of the Requirements for

the Degree of Master of Engineering

in Electronics & Communication

December 2014

.

Table of Contents

Title Page i

CERTIFICATE ii

COMPLIANCE CERTIFICATE iii

THESIS APPROVAL CERTIFICATE iv

DECLARATION OF ORIGINALITY v

Acknowledgment vi

Table of Contents vii

List of Figures x

Abstract xiii

Chapter 1 Introduction 1

1.1 Edge Detection: Analysis 3

1.1.1 Fuzzy Logic in Image Processing 4

1.1.2 Fuzzy Logic for Edge Detection 5

1.1.3 Cellular Learning Automata 6

Chapter 2 Literature Review 7

2.1 Edge Detection: Methodology 7

2.1.1 First Order Derivative Edge Detection 7

2.1.1.1 Prewitts Operator 7

2.1.1.2 [pic] Sobel Operator 8

2.1.1.3 Roberts Cross Operator 11

2.1.1.4 Threshold Selection 11

2.1.2 Second Order Derivative Edge Detection 11

2.1.2.1 Marr-Hildreth Edge Detector 11

2.1.2.2 Canny Edge Detector 12

2.1.3 Soft Computing Approaches to Edge Detection 13

2.1.3.1 Fuzzy Based Approach 14

2.1.3.2 Genetic Algorithm Approach 14

2.1.4 Cellular Learning Automata 15

Chapter 3 Fuzzy Image Processing 18

3.1 Need for Fuzzy Image Processing 19

3.2 Introduction to Fuzzy sets and Crisp sets 20

3.2.1 Classical sets (Crisp sets) 20

3.2.2 Fuzzy sets 21

3.3 Fuzzification 22

3.4 Membership Value Assignment 22

3.5 Defuzzification 23

3.6 Enhancing Edges Using Cellular Learning Automata 26

3.6.1 Divide the Edgy Image Into Overlapping 3 × 3 Windows 26

3.6.2 Penalty and Rewards. 27

Chapter 4 Implementation 30

4.1 Simple algorithm for edge detection using Fuzzy Logic 30

Chapter 5 Conclusion 38

References 39

Appendix A : Acronyms 41

Appendix B : Review Card 42

Appendix C: Compliance Report of Review Card 43

List of Figures

Figure 1 Laplacian of Gaussian Zero...