LEADER 05298nam 2200661Ia 450 001 9910143747003321 005 20170815115547.0 010 $a1-280-41116-3 010 $a9786610411160 010 $a0-470-03489-0 010 $a0-470-03488-2 035 $a(CKB)1000000000356104 035 $a(EBL)255726 035 $a(OCoLC)71521689 035 $a(SSID)ssj0000102843 035 $a(PQKBManifestationID)11122483 035 $a(PQKBTitleCode)TC0000102843 035 $a(PQKBWorkID)10060379 035 $a(PQKB)10886747 035 $a(MiAaPQ)EBC255726 035 $a(CaSebORM)9780470854914 035 $a(EXLCZ)991000000000356104 100 $a20050927d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAnalog VLSI circuits for the perception of visual motion$b[electronic resource] /$fAlan Stocker 205 $a1st edition 210 $aHoboken NJ $cJohn Wiley & Sons$d2006 215 $a1 online resource (243 p.) 300 $aDescription based upon print version of record. 311 $a0-470-85491-X 320 $aIncludes bibliographical references and index. 327 $aAnalog VLSI Circuits for the Perception of Visual Motion; Contents; 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; 2.4.1 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 Perception System; 2.7 Review of a VLSI Implementations; 3 Optimization Networks; 3.1 Associative Memory and Optimization 327 $a3.2 Constraint Satisfaction Problems3.3 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 conductances; 4.3 Simulation Results for Natural Image Sequences; 4.4 Passive Non-linear Network Conductances; 4.5 Extended Recurrent Network Architectures 327 $a4.5.1 Motion segmentation4.5.2 Attention and motion selection; 4.6 Remarks; 5 Analog VLSI Implementation; 5.1 Implementation Substrate; 5.2 Phototransduction; 5.2.1 Logarithmic adaptive photoreceptor; 5.2.2 Robust brightness constancy constraint; 5.3 Extraction of the Spatio-temporal 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 327 $a6.1.1 Speed tuning6.1.2 Contrast dependence; 6.1.3 Spatial frequency tuning; 6.1.4 Orientation tuning; 6.2 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 Human-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 327 $a7.2.2 Non-linear diffusion length7.2.3 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; Appendix; A Variational Calculus; B Simulation Methods; C Transistors and Basic Circuits; D Process Parameters and Chips Specifications; References; Index 330 $aAlthough 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 neural systems, especially for visual processing, has allowed engineers to better understand how complex networks can effectively process large amounts of information, whilst dealing with difficult computational challenges. Analog and parallel processing are key characteristics of biological neural networks. Analog VL 606 $aComputer vision 606 $aMotion perception (Vision)$xComputer simulation 606 $aNeural networks (Computer science) 608 $aElectronic books. 615 0$aComputer vision. 615 0$aMotion perception (Vision)$xComputer simulation. 615 0$aNeural networks (Computer science) 676 $a006.37 686 $a54.72$2bcl 700 $aStocker$b Alan$0946290 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910143747003321 996 $aAnalog VLSI circuits for the perception of visual motion$92137934 997 $aUNINA