Nonlinear Vision: Determination of Neural Receptive Fields, Function, and Networks

Inbunden, Engelska, 2017

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This text brings to vision research a treatment different from that often found in books on the subject in its emphasis on nonlinear aspects of vision, from human perception to eye cells of the fly. There is considerable emphasis on mathematics, which forms not only models but the algorithms for processing data.

Produktinformation

Utgivningsdatum2017-12-13
Mått178 x 254 x undefined mm
Vikt1 200 g
FormatInbunden
SpråkEngelska
Antal sidor563
FörlagTaylor & Francis Ltd
ISBN9781315895963

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1. Nonlinear Systems Analysis in Vision: Overview of Kernal Methods 2. Nonlinear Operator Network Models of Processing in the Fly Lamina 3. Linear and Nonlinear Mechanisms of Motion Selectivity in Simple Cells of Cat's Striate Cortex 4. Nonlinear Functional Representations for Motion Detection and Speed Estimation Schemes 5. Optimizing the Estimation of Nonlinear Kernels 6. A Deterministic Approach to Nonlinear Systems Analysis 7. Applications to the Study of Visual Systems of the Fast Orthogonal Search and Parallel Cascade Methods 8. Alterations of Form Vision at Isoluminance Identified Through the Use of Visual Textures 9. A Frequency Domain Method for Isolating Specific Kinds of Nonlinear Neural Processing and for Testing Nonlinear Multineuron Models Against Data 10. Nonlinearities in Psychophysical Models of the Processing of Spatial Form and Motion 11. A Nonlinear Model of Feature Detection 12. Spatial and Temporal Nonlinearities in Receptive Fields of the Cat Striate Cortex 13. How Flying Bees Compute Range from Optical Flow: Behavioural Experiments and Neural Models 14. Retinotopic Vision in the Locust 15. The Trade-Off Between Resolution and Sensitivity in Compound Eyes 16. Nonlinear Lateral Inhibition Applied to Motion Detection in the Fly Visual System 17. Lateral Inhibition and Image Processing 18. Electronic Hardware for Vision Modeling 19. Multiplicative Inhibition and Volterra Series Expansion 20. INFANT Neural Controller for Adaptive Sensory-Motor Coordination 21. A Neural Network Architecture for Figure-Ground Separation of Connected Scenic Figures.