Next Generation Artificial Vision Systems. Reverse Engineering the Human Visual System

453 Pages·2016·13.1 MB·English

by  Bratath A.

Next Generation Artificial Vision Systems
Reverse Engineering the Human Visual System
Artech House Series
Bioinformatics & Biomedical Imaging
Series Editors
Stephen T. C. Wong, The Methodist Hospital Research Institute
Guang-Zhong Yang, Imperial College
Advances in Diagnostic and Therapeutic Ultrasound Imaging, Jasjit S. Suri,
Chirinjeev Kathuria, Ruey-Feng Chang, Filippo Molinari, and Aaron Fenster, editors
Biological Database Modeling, Jake Chen and Amandeep S. Sidhu, editors
Biomedical Informatics in Translational Research, Hai Hu, Michael Liebman, and
Richard Mural
Genome Sequencing Technology and Algorithms, Sun Kim, Haixu Tang, and
Elaine R. Mardis, editors
Life Science Automation Fundamentals and Applications, Mingjun Zhang,
Bradley Nelson, and Robin Felder, editors
Microscopic Image Analysis for Life Science Applications, Jens Rittscher,
Stephen T. C. Wong, and Raghu Machiraju, editors
Next Generation Artificial Vision Systems: Reverse Engineering the Human Visual
System, Maria Petrou and Anil Bharath, editors
Systems Bioinformatics: An Engineering Case-Based Approach, Gil Alterovitz and
Marco F. Ramoni, editors
Next Generation Artificial Vision Systems
Reverse Engineering the Human Visual System
Anil Bharath
Maria Petrou
Imperial College London
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ISBN 13 978-1-59693-224-1
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10 9 8 7 6 5 4 3 2 1
Contents
Preface xiii
CHAPTER 1
The Human Visual System: An Engineering Challenge 1
1.1 Introduction 1
1.2 Overview of the Human Visual System 2
1.2.1 The Human Eye 3
1.2.1.1 Issues to Be Investigated 8
1.2.2 Lateral Geniculate Nucleus (LGN) 10
1.2.3 The V1 Region of the Visual Cortex 12
1.2.3.1 Issues to Be Investigated 14
1.2.4 Motion Analysis and V5 15
1.2.4.1 Issues to Be Investigated 15
1.3 Conclusions 15
References 17
PART I
The Physiology and Psychology of Vision 19
CHAPTER 2
Retinal Physiology and Neuronal Modeling 21
2.1 Introduction 21
2.2 Retinal Anatomy 21
2.3 Retinal Physiology 25
2.4 Mathematical Modeling----Single Cells of the Retina 27
2.5 Mathematical Modeling----The Retina and Its Functions 28
2.6 A Flexible, Dynamical Model of Retinal Function 30
2.6.1 Foveal Structure 31
2.6.2 Differential Equations 32
2.6.3 Color Mechanisms 34
2.6.4 Foveal Image Representation 36
2.6.5 Modeling Retinal Motion 37
2.7 Numerical Simulation Examples 38
2.7.1 Parameters and Visual Stimuli 38
2.7.2 Temporal Characteristics 39
2.7.3 Spatial Characteristics 41
2.7.4 Color Characteristics 43
2.8 Conclusions 45
References 46
v
vi Contents
CHAPTER 3
A Review of V1 51
3.1 Introduction 51
3.2 Two Aspects of Organization and Functions in V1 52
3.2.1 Single-Neuron Responses 52
3.2.2 Organization of Individual Cells in V1 53
3.2.2.1 Orientation Selectivity 55
3.2.2.2 Color Selectivity 56
3.2.2.3 Scale Selectivity 57
3.2.2.4 Phase Selectivity 58
3.3 Computational Understanding of the Feed Forward V1 58
3.3.1 V1 Cell Interactions and Global Computation 59
3.3.2 Theory and Model of Intracortical Interactions
in V1 61
3.4 Conclusions 62
References 63
CHAPTER 4
Testing the Hypothesis That V1 Creates a Bottom-Up
Saliency Map 69
4.1 Introduction 69
4.2 Materials and Methods 73
4.3 Results 75
4.3.1 Interference by Task-Irrelevant Features 76
4.3.2 The Color-Orientation Asymmetry in Interference 81
4.3.3 Advantage for Color-Orientation Double Feature but
Not Orientation-Orientation Double Feature 84
4.3.4 Emergent Grouping of Orientation Features by Spatial
Configurations 87
4.4 Discussion 92
4.5 Conclusions 98
References 99
PARTII
The Mathematics of Vision 103
CHAPTER 5
V1 Wavelet Models and Visual Inference 105
5.1 Introduction 105
5.1.1 Wavelets 105
5.1.2 Wavelets in Image Analysis and Vision 107
5.1.3 Wavelet Choices 107
5.1.4 Linear vs Nonlinear Mappings 112
5.2 A Polar Separable Complex Wavelet Design 113
Contents vii
5.2.1 Design Overview 113
5.2.2 Filter Designs: Radial Frequency 114
5.2.3 Angular Frequency Response 116
5.2.4 Filter Kernels 118
5.2.5 Steering and Orientation Estimation 119
5.3 The Use of V1-Like Wavelet Models in Computer
Vision 120
5.3.1 Overview 120
5.3.2 Generating Orientation Maps 121
5.3.3 Corner Likelihood Response 123
5.3.4 Phase Estimation 123
5.4 Inference from V1-Like Representations 124
5.4.1 Vector Image Fields 125
5.4.2 Formulation of Detection 126
5.4.3 Sampling of (B,X) 127
5.4.4 The Notion of ‘‘Expected’’ Vector Fields 128
5.4.5 An Analytic Example: Uniform Intensity
Circle 129
5.4.6 Vector Model Plausibility and Extension 129
5.4.7 Vector Fields: A Variable Contrast
Model 130
5.4.8 Plausibility by Demonstration 131
5.4.9 Plausibility from Real Image Data 132
5.4.10 Divisive Normalization 133
5.5 Evaluating Shape Detection Algorithms 135
5.5.1 Circle-and-Square Discrimination Test 135
5.6 Grouping Phase-Invariant Feature Maps 138
5.6.1 Keypoint Detection Using DTCWT 138
5.7 Summary and Conclusions 140
References 141
CHAPTER 6
Beyond the Representation of Images by Rectangular Grids 145
6.1 Introduction 145
6.2 Linear Image Processing 145
6.2.1 Interpolation of Irregularly Sampled Data 146
6.2.1.1 Kriging 146
6.2.1.2 Iterative Error Correction 151
6.2.1.3 Normalized Convolution 153
6.2.2 DFT from Irregularly Sampled Data 156
6.3 Nonlinear Image Processing 157
6.3.1 V1-Inspired Edge Detection 158
6.3.2 Beyond the Conventional Data Representations
and Object Descriptors 162
6.3.2.1 The Trace Transform 162
6.3.2.2 Features from the Trace Transform 165
viii Contents
6.4 Reverse Engineering Some Aspect of the Human Visual
System 167
6.5 Conclusions 168
References 169
CHAPTER 7
Reverse Engineering of Human Vision:
Hyperacuity and Super-Resolution 171
7.1 Introduction 171
7.2 Hyperacuity and Super-Resolution 172
7.3 Super-Resolution Image Reconstruction Methods 173
7.3.1 Constrained Least Squares Approach 174
7.3.2 Projection onto Convex Sets 177
7.3.3 Maximum A Posteriori Formulation 180
7.3.4 Markov Random Field Prior 180
7.3.5 Comparison of the Super-Resolution
Methods 183
7.3.6 Image Registration 183
7.4 Applications of Super-Resolution 184
7.4.1 Application in Minimally Invasive Surgery 184
7.4.2 Other Applications 187
7.5 Conclusions and Further Challenges 188
References 188
CHAPTER 8
Eye Tracking and Depth from Vergence 191
8.1 Introduction 191
8.2 Eye-Tracking Techniques 192
8.3 Applications of Eye Tracking 195
8.3.1 Psychology/Psychiatry and Cognitive Sciences 195
8.3.2 Behavior Analysis 196
8.3.3 Medicine 197
8.3.4 Human--Computer Interaction 199
8.4 Gaze-Contingent Control for Robotic Surgery 200
8.4.1 Ocular Vergence for Depth Recovery 202
8.4.2 Binocular Eye-Tracking Calibration 204
8.4.3 Depth Recovery and Motion Stabilization 206
8.5 Discussion and Conclusions 209
References 210
CHAPTER 9
Motion Detection and Tracking by Mimicking
Neurological Dorsal/Ventral Pathways 217
9.1 Introduction 217
9.2 Motion Processing in the Human Visual System 218
9.3 Motion Detection 219
Contents ix
9.3.1 Temporal Edge Detection 221
9.3.2 Wavelet Decomposition 224
9.3.3 The Spatiotemporal Haar Wavelet 225
9.3.4 Computational Cost 230
9.4 Dual-Channel Tracking Paradigm 230
9.4.1 Appearance Model 231
9.4.2 Early Approaches to Prediction 232
9.4.3 Tracking by Blob Sorting 233
9.5 Behavior Recognition and Understanding 237
9.6 A Theory of Tracking 239
9.7 Concluding Remarks 241
References 242
PART III
Hardware Technologies for Vision 249
CHAPTER 10
Organic and Inorganic Semiconductor Photoreceptors
Mimicking the Human Rods and Cones 251
10.1 Introduction 251
10.2 Phototransduction in the Human Eye 253
10.2.1 The Physiology of the Eye 253
10.2.2 Phototransduction Cascade 255
10.2.2.1 Light Activation of the Cascade 257
10.2.2.2 Deactivation of the Cascade 258
10.2.3 Light Adaptation of Photoreceptors:
Weber-Fechner’s Law 258
10.2.4 Some Engineering Aspects of Photoreceptor Cells 259
10.3 Phototransduction in Silicon 260
10.3.1 CCD Photodetector Arrays 262
10.3.2 CMOS Photodetector Arrays 263
10.3.3 Color Filtering 265
10.3.4 Scaling Considerations 268
10.4 Phototransduction with Organic Semiconductor Devices 269
10.4.1 Principles of Organic Semiconductors 270
10.4.2 Organic Photodetection 271
10.4.3 Organic Photodiode Structure 273
10.4.4 Organic Photodiode Electronic Characteristics 274
10.4.4.1 Photocurrent and Efficiency 274
10.4.4.2 The Equivalent Circuit and Shunt Resistance 277
10.4.4.3 Spectral Response Characteristics 281
10.4.5 Fabrication 281
10.4.5.1 Contact Printing 282
10.4.5.2 Printing on CMOS 284
10.5 Conclusions 285
References 286