Neural Networks for Perception

Contents of Volume 2: Computation, Learning, and Architectures

Contributors

Foreword

Part 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 Space

Part 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 Processing

Index