Neural Networks for Vision and Image Processing

Edited by Gail A. Carpenter and Stephen Grossberg

A Bradford Book

Overview

Author(s)

Summary

This interdisciplinary survey brings together recent models and experiments on how the brain sees and learns to recognize objects. It shows how to use these insights in technology and describes how neural networks provide a unifying computational framework for reaching these goals. Several chapters describe experiments in neurobiology and visual perception that clarify properties of biological vision and key conceptual issues that biological models need to address. Other chapters describe neural and computational models of biological vision that address such issues and clarify processes whereby biological vision derives its remarkable flexibility and power. Still other chapters use biologically derived models or heuristics to suggest neural network solutions to challenging technological problems in computer vision. Topics range from analyses of motion, depth, color and form to new concepts about learning, attention, pattern recognition, and hardware implementation.

Paperback

Out of Print ISBN: 9780262531085 486 pp. | 7 in x 9.8 in

Editors

Gail A. Carpenter

Gail A. Carpenter is Professor of Mathematics and Cognitive and Neural Systems and Director of the CNS Technology Lab at Boston University.

Stephen Grossberg

Stephen Grossberg is Professor of Mathematics, Psychology, and Biomedical Engineering and Director of the Center for Adaptive Systems at Boston University.