Next generation artificial vision systems: reverse engineering the human visual system
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Boston
Artech House
2008
|
| Schriftenreihe: | Artech House bioinformatics & biomedical imaging series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIV, 438 S. Ill. |
| ISBN: | 9781596932241 1596932244 |
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MARC
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Datensatz im Suchindex
| _version_ | 1804142944577388544 |
|---|---|
| adam_text | Contents
Preface xiii
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 VI 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
The Physiology and Psychology of Vision 19
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
A Review of V1 51
3.1 Introduction 51
3.2 Two Aspects of Organization and Functions in VI 52
3.2.1 Single-Neuron Responses 52
3.2.2 Organization of Individual Cells in VI 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 VI 58
3.3.1 VI Cell Interactions and Global Computation 59
3.3.2 Theory and Model of Intracortical Interactions
in VI 61
3.4 Conclusions 62
References 63
Testing the Hypothesis That VI 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
The Mathematics of Vision 103
VI 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
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 Vl-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 Vl-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
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 Vl-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
6.4 Reverse Engineering Some Aspect of the Human Visual
System 167
6.5 Conclusions 168
References 169
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
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
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
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
Hardware Technologies for Vision 249
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
Analog Retinomorphic Circuitry to Perform Retinal
and Retinal-Inspired Processing 289
11.1 Introduction 289
11.2 Principles of Analog Processing 290
11.2.1 The Metal Oxide Semiconductor Field Effect Transistor 292
11.2.1.1 Transistor Operation 293
11.2.1.2 nMOS and pMOS Devices 293
11.2.1.3 Transconductance Characteristics 293
11.2.1.4 Inversion Characteristics 294
11.2.1.5 MOSFET Weak Inversion and Biological
Gap Junctions 295
11.2.2 Analog vs Digital Methodologies 296
11.3 Photo Electric Transduction 296
11.3.1 Logarithmic Sensors 297
11.3.2 Feedback Buffers 298
11.3.3 Integration-Based Photodetection Circuits 298
11.3.4 Photocurrent Current-Mode Readout 300
11.4 Retinimorphic Circuit Processing 300
11.4.1 Voltage Mode Resistive Networks 301
11.4.1.1 Limitations with This Approach 303
11.4.2 Current Mode Approaches to Receptive Field
Convolution 303
11.4.2.1 Improved Horizontal Cell Circuitry 305
11.4.2.2 Novel Bipolar Circuitry 305
11.4.2.3 Bidirectional Current Mode Processing 306
11.4.2.4 Dealing with Multiple High Impedance
Processing Channels 307
11.4.2.5 The Current Comparator 310
11.4.3 Reconfigurable Fields 312
11.4.4 Intelligent Ganglion Cells 314
11.4.4.1 ON-OFF Ganglion Cells 315
11.4.4.2 Pulse Width Encoding 316
11.5 Address Event Representation 317
11.5.1 The Arbitration Tree 318
11.5.2 Collisions 322
11.5.3 Sparse Coding 322
11.5.4 Collision Reduction 323
11.6 Adaptive Foveation 324
11.6.1 System Algorithm 325
11.6.2 Circuit Implementation 326
11.6.3 The Future 329
11.7 Conclusions 330
References 330
Analog VI Platforms 335
12.1 Analog Processing: Obsolete? 335
12.2 The Cellular Neural Network 340
12.3 The Linear CNN 340
12.4 CNNs and Mixed Domain Spatiotemporal Transfer Functions 342
12.5 Networks with Temporal Derivative Diffusion 345
12.5.1 Stability 348
12.6 A Signal Flow Graph-Based Implementation 349
12.6.1 Continuous Time Signal Flow Graphs 349
12.6.2 On SFG Relations with the MLCNN 352
12.7 Examples 355
12.7.1 A Spatiotemporal Cone Filter 355
12.7.2 Visual Cortical Receptive Field Modelling 360
12.8 Modeling of Complex Cell Receptive Fields 362
12.9 Summary and Conclusions 363
References 364
From Algorithms to Hardware Implementation 367
13.1 Introduction 367
13.2 Field Programmable Gate Arrays 367
13.2.1 Circuit Design 369
13.2.2 Design Process 369
13.3 Mapping Two-Dimensional Filters onto FPGAs 369
13.4 Implementation of Complex Wavelet Pyramid on FPGA 370
13.4.1 FPGA Design 370
13.4.2 Host Control 373
13.4.3 Implementation Analysis 374
13.4.4 Performance Analysis 375
13.4.4.1 Corner Detection 377
13.4.5 Conclusions 377
13.5 Hardware Implementation of the Trace Transform 377
13.5.1 Introduction to the Trace Transform 377
13.5.2 Computational Complexity 381
13.5.3 Full Trace Transform System 382
13.5.3.1 Acceleration Methods 382
13.5.3.2 Target Board 383
13.5.3.3 System Overview 383
13.5.3.4 Top-Level Control 384
13.5.3.5 Rotation Block 384
13.5.3.6 Functional Blocks 386
13.5.3.7 Initialization 386
13.5.4 Flexible Functional for Exploration 387
13.5.4.1 Type A Functional Block 388
13.5.4.2 Type B Functional Block 388
13.5.4.3 Type C Functional Block 389
13.5.5 Functional Coverage 389
13.5.6 Performance and Area Results 389
13.5.7 Conclusions 391
13.6 Summary 391
References 392
Real-Time Spatiotemporal Saliency 395
14.1 Introduction 395
14.2 The Framework Overview 396
14.3 Realization of the Framework 398
14.3.1 Two-Dimensional Feature Detection 398
14.3.2 Feature Tracker 399
14.3.3 Prediction 404
14.3.4 Distribution Distance 406
14.3.5 Suppression 410
14.4 Performance Evaluation 411
14.4.1 Adaptive Saliency Responses 411
14.4.2 Complex Scene Saliency Analysis 412
14.5 Conclusions 413
References 413
Acronyms and Abbreviations 415
About the Editors 419
List of Contributors 420
Index 423
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| id | DE-604.BV036455699 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:39:49Z |
| institution | BVB |
| isbn | 9781596932241 1596932244 |
| language | English |
| lccn | 2008298871 |
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| physical | XIV, 438 S. Ill. |
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| series2 | Artech House bioinformatics & biomedical imaging series |
| spelling | Next generation artificial vision systems reverse engineering the human visual system Anil Bharath; Maria Petrou Boston Artech House 2008 XIV, 438 S. Ill. txt rdacontent n rdamedia nc rdacarrier Artech House bioinformatics & biomedical imaging series Künstliche Intelligenz Computer vision Artificial intelligence Signalverarbeitung (DE-588)4054947-1 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Maschinelles Sehen (DE-588)4129594-8 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf Maschinelles Sehen (DE-588)4129594-8 s Mustererkennung (DE-588)4040936-3 s Künstliche Intelligenz (DE-588)4033447-8 s Signalverarbeitung (DE-588)4054947-1 s DE-604 Bharath, Anil Sonstige oth Petrou, Maria Sonstige oth HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020327704&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Next generation artificial vision systems reverse engineering the human visual system Künstliche Intelligenz Computer vision Artificial intelligence Signalverarbeitung (DE-588)4054947-1 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Maschinelles Sehen (DE-588)4129594-8 gnd Mustererkennung (DE-588)4040936-3 gnd |
| subject_GND | (DE-588)4054947-1 (DE-588)4033447-8 (DE-588)4129594-8 (DE-588)4040936-3 |
| title | Next generation artificial vision systems reverse engineering the human visual system |
| title_auth | Next generation artificial vision systems reverse engineering the human visual system |
| title_exact_search | Next generation artificial vision systems reverse engineering the human visual system |
| title_full | Next generation artificial vision systems reverse engineering the human visual system Anil Bharath; Maria Petrou |
| title_fullStr | Next generation artificial vision systems reverse engineering the human visual system Anil Bharath; Maria Petrou |
| title_full_unstemmed | Next generation artificial vision systems reverse engineering the human visual system Anil Bharath; Maria Petrou |
| title_short | Next generation artificial vision systems |
| title_sort | next generation artificial vision systems reverse engineering the human visual system |
| title_sub | reverse engineering the human visual system |
| topic | Künstliche Intelligenz Computer vision Artificial intelligence Signalverarbeitung (DE-588)4054947-1 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Maschinelles Sehen (DE-588)4129594-8 gnd Mustererkennung (DE-588)4040936-3 gnd |
| topic_facet | Künstliche Intelligenz Computer vision Artificial intelligence Signalverarbeitung Maschinelles Sehen Mustererkennung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020327704&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT bharathanil nextgenerationartificialvisionsystemsreverseengineeringthehumanvisualsystem AT petroumaria nextgenerationartificialvisionsystemsreverseengineeringthehumanvisualsystem |