Analog Networks for The Perception of Visual Motion, w. CD-ROM
(Sprache: Englisch)
Neuromorphic engineering is based on the design and fabrication of artificial neural systems, such as vision chips, head-eye systems, and roving robots, whose architecture and principles are based on those of biological nervous systems. Analog Networks for...
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Neuromorphic engineering is based on the design and fabrication of artificial neural systems, such as vision chips, head-eye systems, and roving robots, whose architecture and principles are based on those of biological nervous systems. Analog Networks for the Perception of Visual Motion offers a careful analysis of the problems of visual motion perception. It discusses the basic computational issues of these problems and defines network solutions, presenting the design and characterization of analog VLSI implementations for networks that solve these problems. Analog Networks for the Perception of Visual Motion is an up-to-date reference on analog VLSI implementations of visual motion processing algorithms.
Klappentext zu „Analog Networks for The Perception of Visual Motion, w. CD-ROM “
Although it is now possible to integrate many millions of transistors on a single chip, traditional digital circuit technology is now reaching its limits, facing problems of cost and technical efficiency when scaled down to ever-smaller feature sizes. The analysis of biological neutral systems, especially for visual processing, has allowed angineers to better understand how complex network can effictively process large amounts of information, whilst dealing with difficult computational challenges.Analog and parallel processing are key characteristics of biological neutral networks. Analog VLSI circuits using the same features can therefore be developed to emulate brain-style processing. Using standard CMOS technology, they can be cheaply manufactured, permitting efficient industrial and consumer applications in robotics and mobile electronics.
This book explores the theory, design and implementation of analog VLSI circuits, inspired by visual motion processing in biological neutral networks. Using a novel approach pioneered by the author himself, Stocker explains in detail the construction of a series of electronic chips, providing the reader with a valuable practical insight into the technology.
Analog VLSI Circuits for the Perception of Visual Motion:
* analyzes the computational problems in visual motion perception;
* examines the issue of optimization in analog networks through high level processes such as motion segmentation and selective attention;
* demonstrates network implementation in anallog VLSI CMOS technology to provide computationally efficient devices;
* sets out measurements of final hardware implementation;
* illustrates the similarities of the presented circuits with the human visual motion perception system;
* includes an accompanying website with video clips of circuits under real-time visual conditions and additional supplementary material.
With a complete review of all existing neuromorphic analog
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VLSI systems for visual motion sensing, Analog VLSI Circuits for the Perception of Visual Motion is a unique reference for advanced students in electrical engineering, artificial intelligence, robotics and computational neuroscience. It will also be useful for researcher, professionals, and electronics engineers working in the field.
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Inhaltsverzeichnis zu „Analog Networks for The Perception of Visual Motion, w. CD-ROM “
Foreword.Preface.
1 Introduction.
1.1 Artificial Autonomous Systems.
1.2 Neural Computation and Analogue Integrated Circuits.
2 Visual Motion Perception.
2.1 Image Brightness.
2.2 Correspondence Problem.
2.3 Optical Flow.
2.4 Matching Models.
2.4.1 Explicit matching.
2.4.2 Implicit matching.
2.5 Flow Models.
2.5.1 Global motion.
2.5.2 Local motion.
2.5.3 Perceptual bias.
2.6 Outline for a Visual Motion 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.
3.3.1 Network Architecture.
3.3.2 Global convergence and gain.
3.4 Resistive Network.
4 Visual Motion Perception Networks.
4.1 Model for Optical Flow Estimation.
4.1.1 Well-posed optimization problem.
4.1.2 Mechanical equivalent.
4.1.3 Smoothness and sparse data.
4.1.4 Probabilistic formulation.
4.2 Network Architecture.
4.2.1 Non-stationary optimization.
4.2.2 Network conductance's.
4.3 Simulation Results for Natural Image Sequences.
4.4 Passive Non-Linear Network Conductance's.
4.5 Extended Recurrent Network Architectures.
4.5.1 Motion segmentation.
4.5.2 Attention and motion selection.
4.6 Remarks.
5 Analogue VLSI Implementation.
5.1 Implementation Substrate.
5.2 Photo transduction.
5.2.1 Logarithmic adaptive photoreceptor.
5.2.2 Robust brightness constancy constraint.
5.3 Extraction of the Spatiotemporal Brightness Gradients.
5.3.1 Temporal derivative circuits.
5.3.2 Spatial sampling.
5.4 Single Optical Flow Unit.
5.4.1 Wide linear-range multiplier.
5.4.2 Effective bias conductance.
5.4.3 Implementation of the smoothness constraint.
5.5 Layout.
6 Smooth Optical Flow Chip.
6.1 Response Characteristics.
6.1.1 Speed tuning.
6.1.2 Contrast dependence.
6.1.3 Spatial frequency tuning.
6.1.4 Orientation tuning.
6.2
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Intersection-of-constraints Solution.
6.3 Flow Field Estimation.
6.4 Device Mismatch.
6.4.1 Gradient offsets.
6.4.2 Variations across the array.
6.5 Processing Speed.
6.6 Applications.
6.6.1 Sensor modules for robotic applications.
6.6.2 Man-machine interface.
7 Extended Network Implementations.
7.1 Motion Segmentation Chip.
7.1.1 Schematics of the motion segmentation pixel.
7.1.2 Experiments and results.
7.2 Motion Selection Chip.
7.2.1 Pixel schematics.
7.2.2 Non-linear diffusion length.
7.2.3 Experiments and results.
8 Comparison to Human Motion Vision.
8.1 Human vs.. Chip Perception.
8.1.1 Contrast dependent speed perception.
8.1.2 Bias on perceived direction of motion.
8.1.3 Perceptual dynamics.
8.2 Computational Architecture.
8.3 Remarks.
A Variational Calculus.
A.1 Optimization Problem.
A.2 Well-posed.
A.3 Convexity.
A.4 Global Solution with Strictly Convex Integrand.
B Simulation Methods.
B.1 Spatiotemporal gradient estimation.
B.2 Image sequences.
C Transistors and Basic Circuits.
C.1 Large SignalMOSFETModel.
C.2 Differential Pair Circuit.
C.3 Transconductance Amplifiers.
C.3.1 Inverting amplifier.
C.3.2 Differential amplifier.
C.4 References.
D Process Parameters and Chips Specifications.
D.1 AMS 0.8 µmBiCMOS Process.
D.2 Chip Specifications.
References.
Index.
6.3 Flow Field Estimation.
6.4 Device Mismatch.
6.4.1 Gradient offsets.
6.4.2 Variations across the array.
6.5 Processing Speed.
6.6 Applications.
6.6.1 Sensor modules for robotic applications.
6.6.2 Man-machine interface.
7 Extended Network Implementations.
7.1 Motion Segmentation Chip.
7.1.1 Schematics of the motion segmentation pixel.
7.1.2 Experiments and results.
7.2 Motion Selection Chip.
7.2.1 Pixel schematics.
7.2.2 Non-linear diffusion length.
7.2.3 Experiments and results.
8 Comparison to Human Motion Vision.
8.1 Human vs.. Chip Perception.
8.1.1 Contrast dependent speed perception.
8.1.2 Bias on perceived direction of motion.
8.1.3 Perceptual dynamics.
8.2 Computational Architecture.
8.3 Remarks.
A Variational Calculus.
A.1 Optimization Problem.
A.2 Well-posed.
A.3 Convexity.
A.4 Global Solution with Strictly Convex Integrand.
B Simulation Methods.
B.1 Spatiotemporal gradient estimation.
B.2 Image sequences.
C Transistors and Basic Circuits.
C.1 Large SignalMOSFETModel.
C.2 Differential Pair Circuit.
C.3 Transconductance Amplifiers.
C.3.1 Inverting amplifier.
C.3.2 Differential amplifier.
C.4 References.
D Process Parameters and Chips Specifications.
D.1 AMS 0.8 µmBiCMOS Process.
D.2 Chip Specifications.
References.
Index.
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Bibliographische Angaben
- Autor: Alan Stocker
- 2006, 1. Auflage, 240 Seiten, Maße: 25 cm, Gebunden, Englisch
- Verlag: Wiley & Sons
- ISBN-10: 047085491X
- ISBN-13: 9780470854914
Sprache:
Englisch
Pressezitat
"...provides unconventional and fresh perspectives on how to understand perception and build simple artificial perceptual systems using VLSI circuits." ( The Neurimorphic Engineer, March 2007
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