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商品簡介
計算神經系統科學是一個正在興起的研究領域，近年來已經成為許多國家政府資助的研究方向，吸引著許多青年研究人員。本書分析了視覺運動感知的計算問題、模擬網絡的優化方法等，最有特色處在于從大規模集成電路實現的角度分析了視覺運動處理的原理和算法，可以借助大規模集成電路的高集成度、低成本等優勢，進行模擬的并行視覺運動感知。本書的專業性很強，所涉及的問題非常前沿，屬于交叉學科，極具發展潛力，其權威性不言而喻。對于從事神經網絡、人工智能、控制理論的等領域的研究者，本書有很大的參考價值。在我國，這方面的研究還處于起步階段，本書提出的視覺運動集成電路實現方法無疑是一種嶄新的思路。
目次
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
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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