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Neural Networks for Perception, Volume 1: Human and Machine Perception focuses on models for understanding human perception in terms of distributed computation and examples of PDP models for machine perception. This book addresses both theoretical and practical issues related to the feasibility of both explaining human perception and implementing machine perception in terms of neural network models. The book is organized into two parts. The first part focuses on human perception. Topics on network model of object recognition in human vision, the self-organization of functional architecture in the cerebral cortex, and the structure and interpretation of neuronal codes in the visual system are detailed under this part. Part two covers the relevance of neural networks for machine perception. Subjects considered under this section include the multi-dimensional linear lattice for Fourier and Gabor transforms, multiple- scale Gaussian filtering, and edge detection; aspects of invariant pattern and object recognition; and neural network for motion processing. Neuroscientists, computer scientists, engineers, and researchers in artificial intelligence will find the book useful.
Contents of Volume 2: Computation, Learning, and Architectures
Part I Human Perception
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.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
- No. of pages:
- © Academic Press 1991
- 1st November 1991
- Academic Press
- eBook ISBN:
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