Neural Networks for Perception. Computation, Learning, and Architectures

370 Pages·1992·20.813 MB·English

by  Harry Wechsler

NEURAL NETWORKS
for
PERCEFTION
Volume 2
Computation, Learning, and Architectures
Edited by
Hany Wechsler
George Mason university
Fairfax, Virginia
®
ACADEMIC PRESS, INC.
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Copyright © 1992 by Academic Press, Inc.
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Library of Congress Cataloging-in-Publication Data
Neural networks for perception / edited by Harry Wechsler.
p. cm.
Includes bibliographical references and index.
Contents: v. 1. Human and machine perception — v. 2. Computation,
learning, and architecture.
ISBN 0-12-741251-4 (v. 1). — ISBN 0-12-741252-2 (v. 2)
1. Neural networks (Computer science) 2. Perception.
I. Wechsler, Harry.
QA76.87.N485 1991
006.3—dc20 91-24207
CIP
Printed in the United States of America
91 92 93 94 9 8 7 6 5 4 3 2 1
To my daughter, Gabriela Anya
Contents of Volume 1
PARTI
Human Perception
I. Introduction
H, Wechsler
1.1 Visual Cortex: Window on the Biological Basis of Learning and
Memory
L.N. Cooper
1.2 A Network Model of Object Recognition in Human Vision
S. Ed el man
1.3 A Cortically Based Model for Integration in Visual Perception
L.H. Finkel, G.N. Reeke, and CM. Edelman
1.4 The Symmetric Organization of Parallel Cortical Systems for
Form and Motion Perception
S. Grossberg
1.5 The Structure and Interpretation of Neuronal Codes in the Visual
System
BJ. Richmond and L. M'. Optican
1.6 Self-Organization of Functional Architecture in the
Cerebral Cortex
S. Tanaka
1.7 Filters versus Textons in Human and Machine Texture
Discrimination
D. Williams and B. Julesz
1.8 Two-Dimensional Maps and Biological Vision: Representing
Three-Dimensional Space
G.L. Zimmerman
IX
χ Contents of Volume 1
PART II
Machine Perception
II. Introduction
H. Wechsler
11.1 WISARD and Other Weightless Neurons
/. Aleksander
11.2 Multi-Dimensional Linear Lattice for Fourier and Gabor Transforms,
Multiple-Scale Gaussian Filtering, and Edge Detection
J. Ben-Arie
11.3 Aspects of Invariant Pattern and Object Recognition
T. Caelli, M. Ferraro, and E. Barth
11.4 A Neural Network Architecture for Fast On-Line Supervised
Learning and Pattern Recognition
G.A. Carpenter, S. Grossberg, and J. Reynolds
11.5 Neural Network Approaches to Color Vision
A.C. Hurlhert
11.6 Adaptive Sensory-Motor Coordination Through
Self-Consistency
M. Kuperstein
11.7 Finding Boundaries in Images
J. Malik and P. Perona
11.8 Compression of Remotely Sensed Images Using Self-Organizing
Feature Maps
M. Manohar and J.C. Tilton
11.9 Self-Organizing Maps and Computer Vision
E. Oja
11.10 Region Growing Using Neural Networks
T.R. Reed
11.11 Vision and Space-Variant Sensing
G. Sandini and M. Tistarelli
11.12 Learning and Recognizing 3D Objects from Multiple Views in a
Neural System
M. Seiben and A.M. Waxman
11.13 Hybrid Symbolic-Neural Methods for Improved Recognition
Using High-Level Visual Features
G.G. Towell and J.W. Shavlik
11.14 Multiscale and Distributed Visual Representations and Mappings
for Invariant Low-Level Perception
H. Wechsler
Contents of Volume 1 xi
11.15 Symmetry: A Context Free Cue for Foveated Vision
Y. Yeshurun, D. Reisfeld, and H. Wolfson
11.16 A Neural Network for Motion Processing
Υ.Ί. Zhou and R. Chellappa
Contributors
Numbers in parentheses indicate pages on which the authors' contributions
begin.
Dana Z. Anderson (214), Department of Physics and Joint Institute for Lab-
oratory Astrophysics, University of Colorado and National Institute of
Standards and Technology, Boulder, Colorado 80309
Dana H. Ballard (8), Department of Computer Science, University of Roch-
ester, Rochester, New York 14627
Claus Benkert (214), Department of Physics and Joint Institute for Labora-
tory Astrophysics, University of Colorado and National Institute of Stan-
dards and Technology, Boulder, Colorado 80309
David Casasent (253), Electrical and Computer Engineering, Carnegie Mel-
lon University, Pittsburgh, Pennsylvania 15213
H. John Caulfield (282), Center for Applied Optics, University of Alabama,
Hunts ville, Alabama 35899
Vladimir Cherkassky (40), Department of Electrical Engineering, University
of Minnesota, Minneapolis, Minnesota 55455
David D. Crouch (214), University of Colorado and National Institute of
Standards and Technology, Boulder, Colorado 80309
Robert Hecht-Nielsen (65), HNC, Inc., 5501 Oberlin Drive, San Diego,
California 92121
Charles Hester (282), Teledyne Brown Engineering, 300 Sparkman Drive,
Mailstop 60, Huntsville, Alabama 35807
Ho-in Jeon (282), Center for Applied Optics, University of Alabama, Hunts-
ville, Alabama 35899
R. Barry Johnson (282), Center for Applied Optics, University of Alabama,
Huntsville, Alabama 35899
Behrooz Kamgar-Parsi (94), Code 5510, Naval Research Laboratory, Wash-
ington, DC 20375
Behzad Kamgar-Parsi (94), Code 5510, Naval Research Laboratory, Wash-
ington, DC 20375
Jason Kinser (282), Teledyne Brown Engineering, 300 Sparkman Drive,
Mailstop 60, Huntsville, Alabama 35807
Xlll
xjv Contributors
Hossein Lari-Najafi (40), Department of Electrical Engineering, University
of Minnesota, Minneapolis, Minnesota 55455
Jim Mann (310), Digital Integrated Circuits Group, MIT Lincoln Lab, 244
Wood Street, Lexington, Massachusetts 02173
Wolfgang Pölzleitner (111), Joanneum Research Center, Wastiangasse 6, A-
8010 Graz, Austria
Jack Raffel (310), Digital Integrated Circuits Group, MIT Lincoln Lab, 244
Wood Street, Lexington, Massachusetts 02173
Joseph Shamir (282), Center for Applied Optics, University of Alabama,
Hunts ville, Alabama 35899
Victor M. Stern (128), Intelligent Systems Technology Inc., 12048 Winding
Creek Court, Clifton, Virginia 22024
Mark Temmen (282), Teledyne Brown Engineering, 300 Sparkman Drive,
Mailstop 60, Hunts ville, Alabama 35807
Leonard Uhr (147), Department of Computer Sciences, University of Wis-
consin, Madison, Wisconsin 53706
Santosh S. Venkatesh (173), Moore School of Electrical Engineering, Uni-
versity of Pennsylvania, Philadelphia, Pennsylvania 19104
Steven D. Whitehead (8), Department of Computer Science, University of
Rochester, Rochester, New York 14627
Stephen S. Wilson (335), Applied Intelligent Systems, Inc., Ann Arbor,
Michigan 48103
Foreword
Neural Networks for Perception explores perception and the recent research
in neural networks that has advanced our understanding of both human and
machine perception. Perception is a major facet of our senses and provides
us with the essential information needed to broaden our horizons and to
connect us to the surrounding world, enabling safe movement and advanta-
geous manipulation. Far beyond being merely a scientific challenge, the pos-
sibility of emulating the human sense of perception would revolutionize
countless technologies, such as visual tracking and object recognition, ro-
botics and flexible manufacturing, automation and control, and autonomous
navigation for future space missions. As Aristotle noted,
"All men, by nature, desire to know. An indication of this is the delight we
take in our senses, for even apart from their usefulness they are loved for them-
selves and above all others the sense of sight. For not only with a view to
action, but even when we are not going to do anything we prefer seeing to
everything else. The reason is that this, most o fall senses, makes us know and
brings to light many differences between things."
Indeed, reflecting the intricate connection between perception and purpose-
ful activity, many of the papers in this book deal with meaningful tasks.
Meanwhile, we are witnessing the rapid growth of neural networks re-
search as a novel and viable approach to emulating intelligence in general
and to achieving the recognition and perceptual learning functions of vision.
Neural network research is a synergetic endeavor that draws from cognitive
and neuro-sciences, physics, signal processing, and pattern recognition.
Neural networks (NN), also known as artificial neural systems (ANS), are
implemented as parallel and distributed processing (PDP) models of com-
putation consisting of dense interconnections among computational process-
ing elements (PE or "neuron"). The competitive processes that take place
among the PEs enable neural networks to display fault-tolerance and ro-
bustness with respect to noisy and/or incomplete sensory inputs, while al-
lowing graceful degradation with respect to faulty memory storage and in-
ternal processing.
Neural Networks for Perception showcases the work of preeminent prac-
xv
xvi Foreword
titioners in the field of neural networks and enhances our understanding of
what neural networks are and how they can be gainfully employed. It is
organized into two volumes: The first, subtitled Human and Machine Per-
ception, focuses on models for understanding human perception in terms of
distributed computation and examples of PDP models for machine percep-
tion. The second, subtitled Computation, Learning, and Architectures, ex-
amines computational and adaptation problems related to the use of neu-
ronal systems, and the corresponding hardware architectures capable of
implementing neural networks for perception and of coping with the com-
plexity inherent in massively distributed computation.
Perception is just one of the capabilities needed to implement machine
intelligence. The discussion on perception involves, by default, the full range
of dialectics on the fundamentals of both human and machine intelligence.
Normal science and technological development are always conducted within
some predefined paradigm and this work is no exception. The paradigms
attempt to model the everlasting dichotomy of brain and matter using spe-
cific metaphors. One of the metaphors for neural networks is statistical
physics and thermodynamics; nonetheless, some thoughts on the feasibility
and future use of evolution and quantum mechanics are contemplated as
well. NN advancements parallel those underway in artificial intelligence to-
ward the development of perceptual systems. Consequently, the possibility
of hybrid systems, consisting of NN and AI components, is also considered.
Many have postulated possible arguments about what intelligence is and
how it impinges on perception. Apparently, recognition is a basic biological
function crucial to biological systems in recognizing specific patterns and
responding appropriately: antibodies attack foreign intruders; our ears cap-
ture sound and speech; animals have to locate edible plants; and sensory-
motor interactions involved in navigation and manipulation are predicated
on adequate recognition capabilities. Failure to recognize can be fatal; rec-
ognition should therefore be the ultimate goal of the perceptual system, and
indeed, it probably underlies much of what is intelligence.
Albert Szent-Gyorgi said that
"The brain is not an organ of thinking but an organ o sfurvival, like claws and
fangs. It is made in such a way as to make us accept as truth that which is only
advantage. It is an exceptional, almost pathological constitution one has, if one
follows thoughts logically through, regardless of consequences. Such people
make martyrs, apostles, or scientists, and mostly end on the stake, or in a
chair, electric or academic."
The concepts of recognition and reasoning by analogy underlie recent views
on both planning and learning as espoused by the case-based reasoning
methodology.
Perception involves information processing, and one has to address those
descriptive levels along which visual tasks of varying complexity can be ana-