Computational Vision (eBook)

Front Cover 1
Computational Vision 4
Copyright Page 5
Table of Contents 10
Dedication 6
Introduction 14
Chapter 1. Computational Complexity 20
1.1. Computation 22
1.2. Probabilistic Computation 31
1.3. Parallel Computation 33
1.4. Conclusions 36
Chapter 2. Distributed and Multiscale Representations 38
2.1. Frequency Analysis 40
2.2. Scale Space 50
2.3. Cojoint Spatial/Spatial-Frequency Representations 67
2.4. Texture Analysis 104
2.5. Conclusions 112
Chapter 3. Invariance 114
3.1. Geometrical Invariance 116
3.2. Statistical Invariance 151
3.3. Algebraic Invariance 165
3.4. Conclusions 177
Chapter 4. Parallel and Distributed Processing 180
4.1. Artificial Neural Systems 181
4.2. PDP Models and Physical Systems 202
4.3. Conclusions 230
Chapter 5. Intrinsic Representations 232
5.1. Psychophysical Images 234
5.2. Artificial Images 261
5.3. Ethological Images 265
5.4. Conclusions 265
Chapter 6. Active Perception 268
6.1. Dynamic Vision 270
6.2. Ecological Optics 279
6.3. Motion Interpretation 292
6.4. Navigation 305
6.5. Structured Light 311
6.6. Conclusions 319
Chapter 7. Object Recognition 322
7.1. Human Recognition 323
7.2. Two-dimensional Object Recognition 328
7.3. Three-dimensional Object Recognition 344
7.4. Perceptual Modeling 366
7.5. Parallel Distributed Computation 371
7.6. Conclusions 383
Chapter 8. Intelligent Systems 386
8.1. Artificial Intelligence 11
8.2. Spatial Reasoning 408
8.3. Data Fusion 425
8.4. Learning 440
8.5. Conclusions 479
CHapter 9. Parallel Hardware Architectures 482
9.1. Network Topology and Data Communications 483
9.2. Pipelining 487
9.3. Single Instruction Multiple Data (SIMD) 494
9.4. Multiple Instructions Multiple Data (MIMD) 499
9.5. Data-Driven Computation 504
9.6. Conclusions 509
Chapter 10. Epilogue 512
10.1. Plastic Arts 512
10.2. How Direct Is Perception? 517
10.3. Conclusions 521
References 526
Author Index 562
Subject Index 570