
Next Generation Artificial Vision Systems
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Content
- Next Generation Artificial Vision Systems
- Contents
- Preface
- C H A P T E R 1 The Human Visual System: An Engineering Challenge
- 1.1 Introduction
- 1.2 Overview of the Human Visual System
- 1.2.1 The Human Eye
- 1.2.2 Lateral Geniculate Nucleus (LGN)
- 1.2.3 The V1 Region of the Visual Cortex
- 1.2.4 Motion Analysis and V5
- 1.3 Conclusions
- References
- P A R T I The Physiology and Psychology of Vision
- C H A P T E R 2 Retinal Physiology and Neuronal Modeling
- 2.1 Introduction
- 2.2 Retinal Anatomy
- 2.3 Retinal Physiology
- 2.4 Mathematical Modeling----Single Cells of the Retina
- 2.5 Mathematical Modeling----The Retina and Its Functions
- 2.6 A Flexible, Dynamical Model of Retinal Function
- 2.6.1 Foveal Structure
- 2.6.2 Differential Equations
- 2.6.3 Color Mechanisms
- 2.6.4 Foveal Image Representation
- 2.6.5 Modeling Retinal Motion
- 2.7 Numerical Simulation Examples
- 2.7.1 Parameters and Visual Stimuli
- 2.7.2 Temporal Characteristics
- 2.7.3 Spatial Characteristics
- 2.7.4 Color Characteristics
- 2.8 Conclusions
- References
- C H A P T E R 3 A Review of V1
- 3.1 Introduction
- 3.2 Two Aspects of Organization and Functions in V1
- 3.2.1 Single-Neuron Responses
- 3.2.2 Organization of Individual Cells in V1
- 3.3 Computational Understanding of the Feed Forward V1
- 3.3.1 V1 Cell Interactions and Global Computation
- 3.3.2 Theory and Model of Intracortical Interactions in V1
- 3.4 Conclusions
- References
- C H A P T E R 4 Testing the Hypothesis That V1 Creates a Bottom-Up Saliency Map
- 4.1 Introduction
- 4.2 Materials and Methods
- 4.3 Results
- 4.3.1 Interference by Task-Irrelevant Features
- 4.3.2 The Color-Orientation Asymmetry in Interference
- 4.3.3 Advantage for Color-Orientation Double Feature but Not Orientation-Orientation Double Feature
- 4.3.4 Emergent Grouping of Orientation Features by Spatial Configurations
- 4.4 Discussion
- 4.5 Conclusions
- Acknowledgments
- References
- P A R T II The Mathematics of Vision
- C H A P T E R 5 V1 Wavelet Models and Visual Inference
- 5.1 Introduction
- 5.1.1 Wavelets
- 5.1.2 Wavelets in Image Analysis and Vision
- 5.1.3 Wavelet Choices
- 5.1.4 Linear vs Nonlinear Mappings
- 5.2 A Polar Separable Complex Wavelet Design
- 5.2.1 Design Overview
- 5.2.2 Filter Designs: Radial Frequency
- 5.2.3 Angular Frequency Response
- 5.2.4 Filter Kernels
- 5.3 The Use of V1-Like Wavelet Models in Computer Vision
- 5.3.1 Overview
- 5.3.2 Generating Orientation Maps
- 5.3.3 Corner Likelihood Response
- 5.3.4 Phase Estimation
- 5.4 Inference from V1-Like Representations
- 5.4.1 Vector Image Fields
- 5.4.2 Formulation of Detection
- 5.4.3 Sampling of (B,X)
- 5.4.4 The Notion of ''Expected'' Vector Fields
- 5.4.5 An Analytic Example: Uniform Intensity Circle
- 5.4.6 Vector Model Plausibility and Extension
- 5.4.7 Vector Fields: A Variable Contrast Model
- 5.4.8 Plausibility by Demonstration
- 5.4.9 Plausibility from Real Image Data
- 5.4.10 Divisive Normalization
- 5.5 Evaluating Shape Detection Algorithms
- 5.5.1 Circle-and-Square Discrimination Test
- 5.6 Grouping Phase-Invariant Feature Maps
- 5.6.1 Keypoint Detection Using DTCWT
- 5.7 Summary and Conclusions
- References
- C H A P T E R 6 Beyond the Representation of Images by Rectangular Grids
- 6.1 Introduction
- 6.2 Linear Image Processing
- 6.2.1 Interpolation of Irregularly Sampled Data
- 6.2.2 DFT from Irregularly Sampled Data
- 6.3 Nonlinear Image Processing
- 6.3.1 V1-Inspired Edge Detection
- 6.3.2 Beyond the Conventional Data Representations and Object Descriptors
- 6.4 Reverse Engineering Some Aspect of the Human Visual System
- 6.5 Conclusions
- References
- C H A P T E R 7 Reverse Engineering of Human Vision: Hyperacuity and Super-Resolution
- 7.1 Introduction
- 7.2 Hyperacuity and Super-Resolution
- 7.3 Super-Resolution Image Reconstruction Methods
- 7.3.1 Constrained Least Squares Approach
- 7.3.2 Projection onto Convex Sets
- 7.3.3 Maximum A Posteriori Formulation
- 7.3.4 Markov Random Field Prior
- 7.3.5 Comparison of the Super-Resolution Methods
- 7.3.6 Image Registration
- 7.4 Applications of Super-Resolution
- 7.4.1 Application in Minimally Invasive Surgery
- 7.5 Conclusions and Further Challenges
- References
- C H A P T E R 8 Eye Tracking and Depth from Vergence
- 8.1 Introduction
- 8.2 Eye-Tracking Techniques
- 8.3 Applications of Eye Tracking
- 8.3.1 Psychology/Psychiatry and Cognitive Sciences
- 8.3.2 Behavior Analysis
- 8.3.3 Medicine
- 8.3.4 Human--Computer Interaction
- 8.4 Gaze-Contingent Control for Robotic Surgery
- 8.4.1 Ocular Vergence for Depth Recovery
- 8.4.2 Binocular Eye-Tracking Calibration
- 8.4.3 Depth Recovery and Motion Stabilization
- 8.5 Discussion and Conclusions
- References
- C H A P T E R 9 Motion Detection and Tracking by Mimicking Neurological Dorsal/ Ventral Pathways
- 9.1 Introduction
- 9.2 Motion Processing in the Human Visual System
- 9.3 Motion Detection
- 9.3.1 Temporal Edge Detection
- 9.3.2 Wavelet Decomposition
- 9.3.3 The Spatiotemporal Haar Wavelet
- 9.3.4 Computational Cost
- 9.4 Dual-Channel Tracking Paradigm
- 9.4.1 Appearance Model
- 9.4.2 Early Approaches to Prediction
- 9.4.3 Tracking by Blob Sorting
- 9.5 Behavior Recognition and Understanding
- 9.6 A Theory of Tracking
- 9.7 Concluding Remarks
- Acknowledgments
- References
- P A R T III Hardware Technologies for Vision
- C H A P T E R 10 Organic and Inorganic Semiconductor Photoreceptors Mimicking the Human Rods and Cones
- 10.1 Introduction
- 10.2 Phototransduction in the Human Eye
- 10.2.1 The Physiology of the Eye
- 10.2.2 Phototransduction Cascade
- 10.2.3 Light Adaptation of Photoreceptors: Weber-Fechner's Law
- 10.3 Phototransduction in Silicon
- 10.3.1 CCD Photodetector Arrays
- 10.3.2 CMOS Photodetector Arrays
- 10.3.3 Color Filtering
- 10.3.4 Scaling Considerations
- 10.4 Phototransduction with Organic Semiconductor Devices
- 10.4.1 Principles of Organic Semiconductors
- 10.4.2 Organic Photodetection
- 10.4.3 Organic Photodiode Structure
- 10.4.4 Organic Photodiode Electronic Characteristics
- 10.4.5 Fabrication
- 10.5 Conclusions
- References
- C H A P T E R 11 Analog Retinomorphic Circuitry to Perform Retinal and Retinal-Inspired Processing
- 11.1 Introduction
- 11.2 Principles of Analog Processing
- 11.2.1 The Metal Oxide Semiconductor Field Effect Transistor
- 11.2.2 Analog vs Digital Methodologies
- 11.3 Photo Electric Transduction
- 11.3.1 Logarithmic Sensors
- 11.3.2 Feedback Buffers
- 11.3.3 Integration-Based Photodetection Circuits
- 11.3.4 Photocurrent Current-Mode Readout
- 11.4 Retinimorphic Circuit Processing
- 11.4.1 Voltage Mode Resistive Networks
- 11.4.2 Current Mode Approaches to Receptive Field Convolution
- 11.4.3 Reconfigurable Fields
- 11.4.4 Intelligent Ganglion Cells
- 11.5 Address Event Representation
- 11.5.1 The Arbitration Tree
- 11.5.2 Collisions
- 11.5.3 Sparse Coding
- 11.5.4 Collision Reduction
- 11.6 Adaptive Foveation
- 11.6.1 System Algorithm
- 11.6.2 Circuit Implementation
- 11.6.3 The Future
- 11.7 Conclusions
- References
- C H A P T E R 12 Analog V1 Platforms
- 12.1 Analog Processing: Obsolete?
- 12.2 The Cellular Neural Network
- 12.3 The Linear CNN
- 12.4 CNNs and Mixed Domain Spatiotemporal Transfer Functions
- 12.5 Networks with Temporal Derivative Diffusion
- 12.5.1 Stability
- 12.6 A Signal Flow Graph-Based Implementation
- 12.6.1 Continuous Time Signal Flow Graphs
- 12.6.2 On SFG Relations with the MLCNN
- 12.7 Examples
- 12.7.1 A Spatiotemporal Cone Filter
- 12.7.2 Visual Cortical Receptive Field Modelling
- 12.8 Modeling of Complex Cell Receptive Fields
- 12.9 Summary and Conclusions
- Acknowledgments
- References
- C H A P T E R 13 From Algorithms to Hardware Implementation
- 13.1 Introduction
- 13.2 Field Programmable Gate Arrays
- 13.2.1 Circuit Design
- 13.2.2 Design Process
- 13.3 Mapping Two-Dimensional Filters onto FPGAs
- 13.4 Implementation of Complex Wavelet Pyramid on FPGA
- 13.4.1 FPGA Design
- 13.4.2 Host Control
- 13.4.3 Implementation Analysis
- 13.4.4 Performance Analysis
- 13.4.5 Conclusions
- 13.5 Hardware Implementation of the Trace Transform
- 13.5.1 Introduction to the Trace Transform
- 13.5.2 Computational Complexity
- 13.5.3 Full Trace Transform System
- 13.5.4 Flexible Functionals for Exploration
- 13.5.5 Functional Coverage
- 13.5.6 Performance and Area Results
- 13.5.7 Conclusions
- 13.6 Summary
- References
- C H A P T E R 14 Real-Time Spatiotemporal Saliency
- 14.1 Introduction
- 14.2 The Framework Overview
- 14.3 Realization of the Framework
- 14.3.1 Two-Dimensional Feature Detection
- 14.3.2 Feature Tracker
- 14.3.3 Prediction
- 14.3.4 Distribution Distance
- 14.3.5 Suppression
- 14.4 Performance Evaluation
- 14.4.1 Adaptive Saliency Responses
- 14.4.2 Complex Scene Saliency Analysis
- 14.5 Conclusions
- References
- Acronyms and Abbreviations
- About the Editors
- List of Contributors
- Index
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