| Foreword Preface 1 Introduction 1.1 Artificial Autonomous Systems 1.2 Neural Computation and Analog Integrated Circuits 2 Visual Motion Perception 2.1 Image Brightness 2.2 Correspondence Problem 2.3 Optical Flow 2.4 Matching Models 2.5 Flow Models 2.6 Outline for a Visual Motion Perception System 2.7 Review of a VLSI Implementations 3 Optimization Networks 3.1 Associative Memory and Optimization 3.2 Constraint Satisfaction Problems 3.3 Winner-takes-all Networks 3.4 Resistive Network 4 Visual Motion Perception Networks 4.1 Model for Optical Flow Estimation 4.2 Network Architecture 4.3 Simulation Results for Natural Image Sequences 4.4 Passive Non-linear Network Conducatances 4.5 Extended Recurrent Network Architectures 4.6 Remarks 5 Analog VLSI Implementation 5.1 Implementation Substrate 5.2 Phototransduction 5.3 Extraction of the Spatio-temporal Brightness Gradients 5.4 Single Optical Flow Unit 5.5 Layout 6 Smooth Optical Flow Chip 6.1 Response Characteristics 6.2 Intersection-of-constraints Solution 6.3 Flow Field Estimation 6.4 Device Mismatch 6.5 Processing Speed 6.6 Applications 7 Extended Network Implementations 7.1 Motion Segmentation Chip 7.2 Motion Selection Chip 8 Comparison to Human Motion Vision 8.1 Human vs.Chip Perception 8.2 Computational Architecture 8.3 Remarks A Variational Calculus B Simulation Methods C Transistors and Basic Circuits D Process Parameters and Chips Specifications References Index |
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