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.