University Sétif 1 FERHAT ABBAS Faculty of Sciences
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Auteur Djidel,Oussama |
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Titre : A celluar automata optimized ants colony for edge detection with Neighborhood variation Type de document : texte imprimé Auteurs : Djidel,Oussama, Auteur ; Djemame,Sefia, Directeur de thèse Editeur : Setif:UFA Année de publication : 2019 Importance : 1 vol (74 f .) Format : 29 cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Cellular Automata
Edge Detection
Moore Neighborhood Linear Rule
Ant Colony Optimization ACOIndex. décimale : 004 Informatique Résumé : The cellular automaton is an abstract model of computation. Investing the capabilities of cellular automata in image processing has proved to be promising. This paper presents a method for edge detection of binary images based on two dimensional binary cellular automata with extended Moore neighborhood model. This method uses ant colony optimization (ACO) to find the best linear rules of CA that produce edge detection in one-time iteration. The performance of this approach is compared with some existing edge detection techniques and other standard methods. This comparison shows that the proposed method to be very promising for edge detection of binary images with good quality and significantly low execution time. Note de contenu : Sommaire
Chapter 1 INTRODUCTION ................................................................................................ 1
1. Problem Statement ............................................................................................................. 2
1.1 Edge quality ................................................................................................................. 2
1.2 Calculation time ........................................................................................................... 2
2. Proposed solution .............................................................................................................. 2
3. Memory Organization........................................................................................................ 2
Chapter 2 LITERATURE REVIEW ..................................................................................... 3
1. Introduction ......................................................................................................... 3
2. Digital Image Processing ................................................................................................... 4
2.1 Digital Image Processing History ................................................................ 5
2.2 Fundamental Steps in Digital Image Processing .........................................................
2.2.1. Image Acquisition ............................................................................................... 6
2.2.2. Image Enhancement ........................................................................... 10
2.2.3. Image Restoration .............................................................................................. 11
2.2.4. Color Image Processing .................................................................... 11
2.2.5. Wavelets and Multiresolution Processing ......................................................... 12
2.2.6. Compression ...................................................................................................... 12
2.2.7. Morphological Image Processing ...................................................................... 13
2.2.8. Image Segmentation ............................................................................... 13
2.2.9. Representation and Description ......................................................................... 15
2.2.10. Object Recognition ............................................................................ 15
3. Cellular Automata ........................................................................................................... 16
3.1 General Definition ..................................................................................................... 16
3.2 Formal Definition ...................................................................................................... 16
3.2.1. Structure of Neighborhood ................................................................................ 17
3.2.2. CA Boundary Conditions .................................................................................. 18
3.2.3. The Simplest Type of CA .................................................................................. 18
3.2.4. Game of Life CA ............................................................................................... 19
4. Cellular automaton in Digital Image Processing ............................................................. 20
4.1 An Efficient Edge Detection Technique by Two Dimensional Rectangular Cellular Automata [15] ....................................................................................................... 20
4.2 Cellular Edge Detection: Combining Cellular Automata and Cellular Learning Automata [16] ..................................................................................................... 21
4.3 An Edge Detection Method Using Outer Totalistic Cellular Automata [17] ............ 21
4.4 A Cellular Automata based Optimal Edge Detection Technique using Twenty-Five Neighborhood Model [18] ............................................................................................... 22
4.5 Image Segmentation Using Continuous Cellular Automata [19, 20, 21, 22, 23, 24] 22
5. Ants Colony Optimization ............................................................................................... 24
5.1 Introduction ......................................................................................................24
5.2 ACO Algorithm ......................................................................................................... 25
6. Conclusion ....................................................................................................................... 27
Chapter 3 RESEARCH METHODOLOGY ....................................................................... 28
1. Introduction ..................................................................................................................... 28
2. Tools .......................................................................................................29
3. Dataset ..............................................................................................................
4. Edge Detection CA Specification .................................................................................... 31
5. Ant Colony Optimization Implementation ...................................................................... 33
6. The Fitness Function ....................................................................................................... 35
6.1 First Version Using Root Mean Square Error ........................................................... 35
6.2 Second Version: Introducing Image Reduction......................................................... 36
6.3 Third Version: Introducing Structural Similarity Index ............................................ 37
7. ACO Local Optimum ...................................................................................................... 38
8. ACO algorithm ................................................................................................................ 39
9. ACO illustration .............................................................................................................. 40
IV
10. Some ACO Results ........................................................................................................ 42
11. Conclusion ..................................................................................................................... 42
Chapter 4 RESULTS ........................................................................................................... 43
1. Introduction ..................................................................................................................... 43
2. Visual Comparison .......................................................................................................... 44
3. Standard Metrics Comparison ......................................................................................... 50
3.1 Execution time comparison ....................................................................................... 62
4. Conclusion ....................................................................................................................... 63
Chapter 5 CONCLUSION ................................................................................................... 64
1. Contributions of the Study ............................................................................................... 64
2. Future Prospects .............................................................................................................. 65
References ........................................................................................................................... 66
Annex .................................................................................................................................. 70
List of Tables ................................................................................................................... 70
List of Equations .............................................................................................................. 70
List of Figures .................................................................................................................. 71
List of abbreviations .................................................................Côte titre : MAI/0335 En ligne : https://drive.google.com/file/d/1E3z_lWOXdl_hza6h4zUyeh69PW8tzId0/view?usp=shari [...] Format de la ressource électronique : A celluar automata optimized ants colony for edge detection with Neighborhood variation [texte imprimé] / Djidel,Oussama, Auteur ; Djemame,Sefia, Directeur de thèse . - [S.l.] : Setif:UFA, 2019 . - 1 vol (74 f .) ; 29 cm.
Langues : Français (fre)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Cellular Automata
Edge Detection
Moore Neighborhood Linear Rule
Ant Colony Optimization ACOIndex. décimale : 004 Informatique Résumé : The cellular automaton is an abstract model of computation. Investing the capabilities of cellular automata in image processing has proved to be promising. This paper presents a method for edge detection of binary images based on two dimensional binary cellular automata with extended Moore neighborhood model. This method uses ant colony optimization (ACO) to find the best linear rules of CA that produce edge detection in one-time iteration. The performance of this approach is compared with some existing edge detection techniques and other standard methods. This comparison shows that the proposed method to be very promising for edge detection of binary images with good quality and significantly low execution time. Note de contenu : Sommaire
Chapter 1 INTRODUCTION ................................................................................................ 1
1. Problem Statement ............................................................................................................. 2
1.1 Edge quality ................................................................................................................. 2
1.2 Calculation time ........................................................................................................... 2
2. Proposed solution .............................................................................................................. 2
3. Memory Organization........................................................................................................ 2
Chapter 2 LITERATURE REVIEW ..................................................................................... 3
1. Introduction ......................................................................................................... 3
2. Digital Image Processing ................................................................................................... 4
2.1 Digital Image Processing History ................................................................ 5
2.2 Fundamental Steps in Digital Image Processing .........................................................
2.2.1. Image Acquisition ............................................................................................... 6
2.2.2. Image Enhancement ........................................................................... 10
2.2.3. Image Restoration .............................................................................................. 11
2.2.4. Color Image Processing .................................................................... 11
2.2.5. Wavelets and Multiresolution Processing ......................................................... 12
2.2.6. Compression ...................................................................................................... 12
2.2.7. Morphological Image Processing ...................................................................... 13
2.2.8. Image Segmentation ............................................................................... 13
2.2.9. Representation and Description ......................................................................... 15
2.2.10. Object Recognition ............................................................................ 15
3. Cellular Automata ........................................................................................................... 16
3.1 General Definition ..................................................................................................... 16
3.2 Formal Definition ...................................................................................................... 16
3.2.1. Structure of Neighborhood ................................................................................ 17
3.2.2. CA Boundary Conditions .................................................................................. 18
3.2.3. The Simplest Type of CA .................................................................................. 18
3.2.4. Game of Life CA ............................................................................................... 19
4. Cellular automaton in Digital Image Processing ............................................................. 20
4.1 An Efficient Edge Detection Technique by Two Dimensional Rectangular Cellular Automata [15] ....................................................................................................... 20
4.2 Cellular Edge Detection: Combining Cellular Automata and Cellular Learning Automata [16] ..................................................................................................... 21
4.3 An Edge Detection Method Using Outer Totalistic Cellular Automata [17] ............ 21
4.4 A Cellular Automata based Optimal Edge Detection Technique using Twenty-Five Neighborhood Model [18] ............................................................................................... 22
4.5 Image Segmentation Using Continuous Cellular Automata [19, 20, 21, 22, 23, 24] 22
5. Ants Colony Optimization ............................................................................................... 24
5.1 Introduction ......................................................................................................24
5.2 ACO Algorithm ......................................................................................................... 25
6. Conclusion ....................................................................................................................... 27
Chapter 3 RESEARCH METHODOLOGY ....................................................................... 28
1. Introduction ..................................................................................................................... 28
2. Tools .......................................................................................................29
3. Dataset ..............................................................................................................
4. Edge Detection CA Specification .................................................................................... 31
5. Ant Colony Optimization Implementation ...................................................................... 33
6. The Fitness Function ....................................................................................................... 35
6.1 First Version Using Root Mean Square Error ........................................................... 35
6.2 Second Version: Introducing Image Reduction......................................................... 36
6.3 Third Version: Introducing Structural Similarity Index ............................................ 37
7. ACO Local Optimum ...................................................................................................... 38
8. ACO algorithm ................................................................................................................ 39
9. ACO illustration .............................................................................................................. 40
IV
10. Some ACO Results ........................................................................................................ 42
11. Conclusion ..................................................................................................................... 42
Chapter 4 RESULTS ........................................................................................................... 43
1. Introduction ..................................................................................................................... 43
2. Visual Comparison .......................................................................................................... 44
3. Standard Metrics Comparison ......................................................................................... 50
3.1 Execution time comparison ....................................................................................... 62
4. Conclusion ....................................................................................................................... 63
Chapter 5 CONCLUSION ................................................................................................... 64
1. Contributions of the Study ............................................................................................... 64
2. Future Prospects .............................................................................................................. 65
References ........................................................................................................................... 66
Annex .................................................................................................................................. 70
List of Tables ................................................................................................................... 70
List of Equations .............................................................................................................. 70
List of Figures .................................................................................................................. 71
List of abbreviations .................................................................Côte titre : MAI/0335 En ligne : https://drive.google.com/file/d/1E3z_lWOXdl_hza6h4zUyeh69PW8tzId0/view?usp=shari [...] Format de la ressource électronique : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0335 MAI/0335 Mémoire Bibliothéque des sciences Anglais Disponible
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