Neural Networks for Perception

Contents of Volume 2: Computation, Learning, and ArchitecturesContributorsForewordPart I Human Perception I.Introduction 1.1 Visual Cortex: Window on the Biological Basis of Learning and Memory 1.2 A Network Model of Object Recognition in Human Vision 1.3 A Cortically Based Model for Integration in Visual Perception 1.4 The Symmetric Organization of Parallel Cortical Systems for Form and Motion Perception 1.5 The Structure and Interpretation of Neuronal Codes in the Visual System 1.6 Self-Organization of Functional Architecture in the Cerebral Cortex 1.7 Filters Versus Textons in Human and Machine Texture Discrimination 1.8 Two-Dimensional Maps and Biological Vision: Representing Three-Dimensional SpacePart II Machine Perception II.Introduction II.1 WISARD and Other Weightless Neurons II.2 Multi-Dimensional Linear Lattice for Fourier and Gabor Transforms, Multiple-Scale Gaussian Filtering, and Edge Detection II.3 Aspects of Invariant Pattern and Object Recognition II.4 A Neural Network Architecture for Fast On-Line Supervised Learning and Pattern Recognition II.5 Neural Network Approaches to Color Vision II.6 Adaptive Sensory-Motor Coordination Through Self-Consistency II.7 Finding Boundaries in Images II.8 Compression of Remotely Sensed Images Using Self-Organizing Feature Maps II.9 Self-Organizing Maps and Computer Vision II.10 Region Growing Using Neural Networks II.11 Vision and Space-Variant Sensing II.12 Learning and Recognizing 3D Objects from Multiple Views in a Neural System II.13 Hybrid Symbolic-Neural Methods for Improved Recognition Using High-Level Visual Features II.14 Multiscale and Distributed Visual Representations and Mappings for Invariant Low-Level Perception II.15 Symmetry: A Context Free Cue for Foveated Vision II.16 A Neural Network for Motion ProcessingIndex