LEADER 05584nam 2200757Ia 450 001 9911020345303321 005 20240819225136.0 010 $a9786610411160 010 $a9781280411168 010 $a1280411163 010 $a9780470034897 010 $a0470034890 010 $a9780470034880 010 $a0470034882 035 $a(CKB)1000000000356104 035 $a(EBL)255726 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(OCoLC)71521689 035 $a(CaSebORM)9780470854914 035 $a(OCoLC)840427826 035 $a(OCoLC)ocn840427826 035 $a(OCoLC)ocn840427826 035 $a(Perlego)2750698 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 /$fAlan Stocker 205 $a1st edition 210 $aHoboken NJ $cJohn Wiley & Sons$d2006 215 $a1 online resource (243 p.) 300 $aDescription based upon print version of record. 311 08$a9780470854914 311 08$a047085491X 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 517 3 $aAnalog very large-scale integrated circuits for the perception of visual motion 606 $aComputer vision 606 $aMotion perception (Vision)$xComputer simulation 606 $aNeural networks (Computer science) 615 0$aComputer vision. 615 0$aMotion perception (Vision)$xComputer simulation. 615 0$aNeural networks (Computer science) 676 $a006.3/7 686 $a54.72$2bcl 700 $aStocker$b Alan A$0725299 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911020345303321 996 $aAnalog VLSI circuits for the perception of visual motion$91415934 997 $aUNINA LEADER 01752nam0 22003973i 450 001 VAN00292998 005 20251105123257.759 017 70$2N$a9783031607509 100 $a20250516d2024 |0itac50 ba 101 $aeng 102 $aCH 105 $a|||| ||||| 181 $ai$b e 182 $ab 183 $acr 200 1 $aAlternative Energy Resources in the MENA Region$feditor Abdellah Henni, Abdelazim Negm, Djamal Zerrouki 210 $aCham$cSpringer$d2024 215 $aXI, 484 p.$cill.$d24 cm 410 1$1001VAN00115388$12001 $aˆThe ‰Handbook of Environmental Chemistry$1210 $aHeidelberg$cSpringer$d1982-$v131 620 $aCH$dCham$3VANL001889 676 $a577.14$cChimica ambientale$v22 702 1$aHenni$bAbdellah$3VANV248737 702 1$aNegm$bAbdelazim M.$3VANV194648 702 1$aZerrouki$bDjamal$3VANV248738 712 $aSpringer $3VANV108073$4650 790 1$aNegm, Abdelazim$zNegm, Abdelazim M.$3VANV196269 790 1$aNegm, A. 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