Neural Networks for Perception. Human and Machine Perception

✍ Scribed by Harry Wechsler

• Publisher: Elsevier Inc, Academic Press
• Year: 1992
• Tongue: English
• Leaves: 528
• Category: Library

✦ Synopsis

These volumes explore recent research in neural networks that has advanced our understanding of human and machine perception. Contributions from international researchers address both theoretical and practical issues related to the feasibility of neural network models to explain human perception and implement machine perception. Volume 1 covers models for understanding human perception in terms of distributed computation as well as examples of neural network models for machine perception and volume 2 examines computational and adaptational problems related to the use of neural systems and discusses the corresponding hardware architectures needed to implement neural networks for perception

✦ Table of Contents

Content:
Front Matter, Page iii
Copyright, Page iv
Dedication, Page v
Contents of Volume 2, Pages xi-xii
Contributors, Pages xiii-xv
Foreword, Pages xvii-xxi, Harry Wechsler
I - Introduction, Pages 3-7
I.1 - Visual Cortex: Window on the Biological Basis of Learning and Memory, Pages 8-24, LEON N. COOPER
I.2 - A Network Model of Object Recognition in Human Vision, Pages 25-40, S. EDELMAN
I.3 - A Cortically Based Model for Integration in Visual Perception, Pages 41-63, LEIF H. FINKEL, GEORGE N. REEKE JR., GERALD M. EDELMAN
I.4 - THE SYMMETRIC ORGANIZATION OF PARALLEL CORTICAL SYSTEMS FOR FORM AND MOTION PERCEPTION, Pages 64-103, STEPHEN GROSSBERG
I.5 - The Structure and Interpretation of Neuronal Codes in the Visual System, Pages 104-119, BARRY J. RICHMOND, LANCE M. OPTICAN
I.6 - Self-Organization of Functional Architecture in The Cerebral Cortex, Pages 120-144, SHIGERU TANAKA
I.7 - Filters Versus Textons in Human and Machine Texture Discrimination, Pages 145-175, DOUGLAS WILLIAMS, BELA JULESZ
I.8 - Two-Dimensional Maps and Biological Vision: Representing Three-Dimensional Space, Pages 176-191, G. Lee Zimmerman
II - Introduction, Pages 195-201
II.1 - WISARD and other Weightless Neurons, Pages 202-213, IGOR ALEKSANDER
II.2 - Multi-Dimensional Linear Lattice for Fourier and Gabor Transforms, Multiple-Scale Gaussian Filtering, and Edge Detection, Pages 214-233, JEZEKIEL BEN-ARIE
II.3 - ASPECTS OF INVARIANT PATTERN AND OBJECT RECOGNITION, Pages 234-247, TERRY CAELLI, MARIO FERRARO, ERHARDT BARTH
II.4 - A NEURAL NETWORK ARCHITECTURE FOR FAST ON-LINE SUPERVISED LEARNING AND PATTERN RECOGNITION, Pages 248-264, GAIL A. CARPENTER, STEPHEN GROSSBERG, JOHN REYNOLDS
II.5 - Neural Network Approaches to Color Vision, Pages 265-284, ANYA C. HURLBERT
II.6 - Adaptive Sensory-Motor Coordination Through Self-Consistency, Pages 285-314, MICHAEL KUPERSTEIN
II.7 - Finding Boundaries in Images, Pages 315-344, JITENDRA MALIK, PIETRO PERONA
II.8 - Compression of Remotely Sensed Images using Self Organizing Feature Maps, Pages 345-367, M. MANOHAR, JAMES C. TILTON
II.9 - Self - Organizing Maps and Computer Vision, Pages 368-385, ERKKI OJA
II.10 - Region Growing Using Neural Networks, Pages 386-397, TODD R. REED
II.11 - Vision and Space-Variant Sensing, Pages 398-425, G. SANDINI, M. TISTARELLI
II.12 - Learning and Recognizing 3D Objects from Multiple Views in a Neural System, Pages 426-444, MICHAEL SEIBERT, ALLEN M. WAXMAN
II.13 - Hybrid Symbolic-Neural Methods for Improved Recognition Using High-Level Visual Features, Pages 445-461, GEOFFREY G. TOWELL, JUDE W. SHAVLIK
II.14 - Multiscale and Distributed Visual Representations and Mappings for Invariant Low-Level Perception, Pages 462-476, HARRY WECHSLER
II.15 - Symmetry: a context free Cue for Foveated Vision, Pages 477-491, YEHEZKEL YESHURUN, DANIEL REISFELD, HAIM WOLFSON
II.16 - A Neural Network for Motion Processing, Pages 492-516, Y.T. ZHOU, R. CHELLAPPA
Index, Pages 517-520