The book presents findings, views and ideas on what exact problems of image processing, pattern recognition and generation can be efficiently solved by cellular automata architectures. This volume provides a convenient collection in this area, in which publications are otherwise widely scattered throughout the literature. The topics covered include image compression and resizing; skeletonization, erosion and dilation; convex hull computation, edge detection and segmentation; forgery detection and content based retrieval; and pattern generation.
The book advances the theory of image processing, pattern recognition and generation as well as the design of efficient algorithms and hardware for parallel image processing and analysis. It is aimed at computer scientists, software programmers, electronic engineers, mathematicians and physicists, and at everyone who studies or develops cellular automaton algorithms and tools for image processing and analysis, or develops novel architectures and implementations of massive parallel computing devices.
The book will provide attractive reading for a general audience because it has do-it-yourself appeal: all the computer experiments presented within it can be implemented with minimal knowledge of programming. The simplicity yet substantial functionality of the cellular automaton approach, and the transparency of the algorithms proposed, makes the text ideal supplementary reading for courses on image processing, parallel computing, automata theory and applications.
Author(s): Rosin, Paul, Adamatzky, Andrew, Sun, Xianfang
Publisher: Springer
Year: 2014
Language: English
Pages: 311
Cover......Page 1
1.1 Introduction and Motivation......Page 15
1.2.1 Principle of Chaotic Scan......Page 19
1.2.2 Properties of the Chaotic Counters......Page 21
1.2.3 Designing Good Chaotic Counters as Hybrid CellularAutomata......Page 24
1.2.4 Message Recovery and Examples......Page 26
1.3.1 The General Framework of Dictionary Based Compression......Page 29
1.3.2 Learning CA-Based Dictionaries and Performance Evaluation of the CA-VQ System......Page 30
1.4 Hardware Description and Synthesis of CA Using AlgebraicNormal Form......Page 34
References......Page 35
2.1 Introduction......Page 38
2.2.1 CA Fundamentals......Page 42
2.2.2 Canny Edge Detector......Page 43
2.3.1 Edge Detection......Page 44
2.3.2 CA Resizing......Page 45
2.3.3 Remapping Process......Page 46
2.4 Experimental Results......Page 47
2.5 Hardware Implementation......Page 53
2.6 Discussion and Conclusions......Page 55
References......Page 56
3.1 Introduction......Page 59
3.2 Skeletonizing Algorithms......Page 61
3.3 Guo and Hall Algorithm......Page 63
3.4 Cellular Automata......Page 65
3.5 Parallel Implementation......Page 67
3.5.1 Examples......Page 68
3.6 Conclusions......Page 72
4.1 Introduction......Page 76
4.2 Mathematical Morphology......Page 78
4.3 Quantum-dot Cellular Automata......Page 80
4.4.2 Circuit Design......Page 84
4.5 QCA Implementation of Morphological Operations......Page 86
4.5.1 QCA Implementation of Morphological Erosion......Page 87
4.5.2 QCA Implementation of Morphological Dilation......Page 88
References......Page 91
5.1 Introduction......Page 96
5.2 Boundary Detection......Page 97
5.3 Edge Detection in Intensity Images......Page 100
5.4 Post-processing of Edges......Page 103
5.4.1 A Simple Edge Linking Scheme......Page 104
5.5 Experiments......Page 105
5.6 Conclusions......Page 111
References......Page 112
Copy-Move Forgery Detection Using CellularAutomata......Page 115
6.1 Introduction......Page 116
6.3 Copy-Move Forgery Detection (CMFD)......Page 117
6.3.1 Block-Based Method for CMFD......Page 119
6.3.2 Possible Feature Vectors......Page 121
6.4.1 Representation of Image in Binary......Page 122
6.4.2 Plain CMF......Page 126
6.4.3 Application on Post-processed Images......Page 129
6.6 Conclusion......Page 132
References......Page 133
7.1 Introduction......Page 136
7.2 Scenario 1: Using Cellular Automata and LUDecomposition......Page 138
7.2.1 Proposed Model......Page 139
7.3.1 Proposed Model......Page 143
7.4 Dataset and Experimental Results......Page 145
7.4.1 Performance and Visual Quality......Page 146
7.4.3 True and False Alert......Page 147
7.4.5 Secret Key Sensitivity......Page 148
7.4.6 Diffusion......Page 150
7.5 Limitations......Page 151
7.6 Conclusion and Future Work......Page 153
References......Page 154
8.2 Content-Based Image Retrieval: A Background......Page 155
8.3.1 Noise Reduction......Page 157
8.3.2 Edge Detection......Page 160
8.3.4 Colour Matching and Histograms......Page 161
8.3.5 Shape Matching......Page 163
8.4 Practical Case Study: Recognition of LEGO Bricks......Page 164
References......Page 168
The Application of Cellular Automaton inMedical Semiautomatic Segmentation......Page 171
9.1 Introduction......Page 172
9.2 Cellular Automaton Segmentation Rule......Page 174
9.3.1 Labels in Image Plane......Page 176
9.3.2 Regional Cellular Automaton Segmentation......Page 177
9.3.3 Volume Cellular Automaton Segmentation......Page 181
9.4 Block Cellular Automaton Segmentation in MedicalApplications......Page 185
References......Page 188
10.1 Notational and Naming Convention......Page 191
10.2 Introduction......Page 192
10.3 The Angular Point of View......Page 194
10.3.2 Complete θ -Convex Hull......Page 195
10.4 The Metrical Point of View......Page 196
10.4.1 Majority Rule and Metric Convexity......Page 198
10.4.2 Complete Metric Convex Hulls......Page 200
10.5.1 Pairwise Construction in Euclidean Space......Page 204
10.5.2 (Metric) Gabriel Graphs in Cellular Spaces......Page 206
10.6 The Complete Cellular Automaton......Page 209
References......Page 211
11.1 Building Envelope and Daylighting......Page 213
11.2 Why Cellular Automata to Drive Shading of BuildingEnvelopes?......Page 215
11.2.1 The Nomenclature......Page 217
11.3 One-Dimensional Cellular Automata Applied on Surfaces......Page 219
11.3.3 Half-Distance Automata......Page 220
11.3.4 Higher Order Cellular Automata......Page 221
11.3.5 Other Regular Tessellations: Hexagonal and Triangular......Page 223
11.3.6 PFSS on Triangular Grid: PFSST......Page 227
11.4 Two-Dimensional Cellular Automata on Surfaces......Page 228
11.4.1 Triangular Cellular Automata......Page 230
11.5 Application of Evolutionary Algorithms for Optimizationof CA Shading......Page 231
11.6 Prototypes......Page 234
References......Page 236
Pattern Formation Using Cellular Automata andL-Systems: A Case Study in Producing IslamicPatterns......Page 240
12.1 Introduction......Page 241
12.2 Preliminary Definitions and Terminologies......Page 244
12.3 Proposed Method......Page 245
12.3.1 Ma’qeli Character Generation Using MargolusNeighborhood......Page 246
12.3.2 Word and Sentence Generation Using Ma’qeli Patterns......Page 247
12.3.3 Holy Word Formation Using L-Systems......Page 248
12.4 Experimental Results......Page 251
12.4.2 Ma’qeli Script Generation Using 2D SynchronousCellular Automata......Page 252
12.4.3 Holy Words Formation Using L-Systems......Page 256
References......Page 257
13.1 Creative Projects Based on Cellular Automata Systems......Page 260
13.2 The Fluid Automata Project......Page 263
13.3.1 Fluid Simulation......Page 264
13.3.2 Flow Visualization......Page 268
13.4 Single-User Interaction Techniques......Page 271
13.5 Multi-user Interaction Techniques......Page 272
13.6 The Annular Genealogy Project......Page 273
13.7 Conclusion......Page 275
References......Page 277
References......Page 280
