Neural Network Systems Techniques and Applications. Volume 5. Image Processing and Pattern Recognition

407 Pages·6.648 MB·English

by  Leondes C.T. (Ed.)

Image Processing and
Pattern Recognition
Neural Network Systems
Techniques and Applications
Edited by Cornelius T. Leondes
VOLUME 1. Algorithms and Architectures
VOLUME 2. Optimization Techniques
VOLUME 3. Implementation Techniques
VOLUME 4. Industrial and Manufacturing Systems
VOLUME 5. Image Processing and Pattern Recognition
VOLUME 6. Fuzzy Logic and Expert Systems Applications
VOLUME 7. Control and Dynamic Systems
Image Processing and
Pattern Recognition
Edited by
Cornelius T. Leondes
Professor Emeritus
University of California
Los Angeles, California
V O L U ME D OF
Neural Network Systems
Techniques and Applications
ACADEMIC PRESS
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Copyright © 1998 by ACADEMIC PRESS
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Library of Congress Card Catalog Number: 97-80441
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PRINTED IN THE UNITED STATES OF AMERICA
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Contents
Contributors xiii
Preface xv
Pattern Recognition
Jouko Lampinen, Jorma Laaksonen, and Erkki Oja
I. Introduction 1
II. Pattern Recognition Problem 3
A. Data Collection 4
B. Registration 5
C. Preprocessing 5
D. Segmentation 5
E. Normalization 6
F. Feature Extraction 7
G. Classification and Clustering 8
H. Postprocessing 8
I. Loop-Backs between Stages 9
J. Trainable Parts in a System 10
III. Neural Networks in Feature Extraction 11
A. Feature Extraction Problem 11
B. Two Classes of Unsupervised Neural Learning 12
C. Unsupervised Back-Propagation 13
D. Nonlinear Principal Component Analysis 16
E. Data Clustering and Compression by the
Self-Organizing Map 17
IV. Classification Methods: Statistical and Neural 20
A. Mathematical Preliminaries 22
B. Density Estimation Methods 23
C. Regression Methods 27
vi Contents
D. Prototype Classifiers 30
E. Subspace Classifiers 32
F. Special Properties of Neural Methods 33
G. Cross-Validation in Classifier Design 35
H. Rejection 36
I. Committees 36
J. On Comparing Classifiers 37
V. Neural Network Applications in Pattern Recognition 38
A. Application Areas of Neural Networks 38
B. Examples of Neural Pattern Recognition Systems 41
VI. Summary 52
References 53
Comparison of Statistical and Neural Classifiers and
Their Applications to Optical Character Recognition
and Speech Classification
Ethem Alpaydtn and Fikret Gurgen
I. Introduction 61
II. Applications 63
III. Data Acquisition and Preprocessing 64
A. Optical Character Recognition 64
B. Speech Recognition 65
IV. statistical Classifiers 65
A. Parametric Bayes Classifiers 67
B. Nonparametric Kernel-Based Density Estimators 70
C. Semiparametric Mixture Models 72
V. Neural Classifiers 74
A. Simple Perceptrons 77
B. Multilayer Perceptrons 78
C. Radial Basis Functions 78
VI. Literature Survey 79
A. Optical Character Recognition 79
B. Speech Recognition 80
VII. Simulation Results 81
VIII. Conclusions 85
References 86
Contents vi:
Medical Imaging
Ying Sun and Reza Nekovei
I. Introduction 89
A. Medical Imaging 90
B. Media Used for Medical Imaging 90
11. Review of Artificial Neural Network Applications
in Medical Imaging 95
A. Model for Medical Image Processing 95
B. Review of Recent Literature 96
III. Segmentation of Arteriograms 99
A. Background 99
B. Problem Statement 101
IV. Back-Propagation Artificial Neural Network for
Arteriogram Segmentation: A Supervised Approach 101
A. Overview of the Feedforward Back-Propagation
Neural Network 101
B. Back-Propagation Artificial Neural Network Classifier
for Arteriogram Segmentation 104
V. Self-Adaptive Artificial Neural Network for Arteriogram
Segmentation: An Unsupervised Approach 107
A. Adaptive Systems and Gradient Search Method 107
B. Derivation of the Self-Adaptive Classifier 109
C. Performance Evaluation of the
Self-Adaptive Classifier 117
VI. Conclusions 124
A. Neural Network Applications in Medical Imaging 124
B. Supervised versus Unsupervised Artificial Neural
Network for Arteriogram Segmentation 127
C. Future Directions 128
References 129
Paper Currency Recognition
Fumiaki Takeda and Sigeru Omatu
I. Introduction 133
II. Small-Size Neuro-Recognition Technique Using
the Masks 134
A. Basic Idea of the Mask Technique 134
Contents
B. Study of the Mask Parameters 137
C. Experiments of the Neural Network Scale
Reduction Using the Masks 142
III. Mask Determination Using the Genetic Algorithm 143
A. Conventional Mask Determination 144
B. Basic Operations of the Genetic Algorithm 147
C. Experiments Using U.S. Dollars 149
IV. Development of the Neuro-Recognition Board Using
the Digital Signal Processor 152
A. Design Issue Using the Conventional Devices 152
B. Basic Architecture of the
Neuro-Recognition Board 152
V. Unification of Three Core Techniques 156
VI. Conclusions 158
References 159
Neural Network Classification Reliability:
Problems and Applications
Luigi P. Cordelia, Carlo Sansone, Francesco Tortorella, Mario Vento,
and Claudio De Stefano
I. Introduction 161
II. Classification Paradigms 164
III. Neural Network Classifiers 167
IV. Classification Reliability 172
V. Evaluating Neural Network
Classification Reliability 174
VI. Finding a Reject Rule 178
A. Method 178
B. Discussion 184
VII. Experimental Results 185
A. Case 1: Handwritten Character Recognition 186
B. Case 2: Fault Detection and Isolation 192
VIII. Summary 196
References 197
Contents ix
Parallel Analog Image Processing: Solving
Regularization Problems with Architecture Inspired
by the Vertebrate Retinal Circuit
Tetsuya Vagi Haruo Kohayashi, and Takashi Matsumoto
I. Introduction 201
II. Physiological Background 202
A. Structure of the Retina 203
B. Circuit Elements 205
C. Outer Retinal Circuit 210
D. Neuronal Adaptation 215
E. Analog Network Model of Outer Retina 215
III. Regularization Vision Chips 221
A. Introduction 221
B. Tikhonov Regularization 221
C. Two-Dimensional Problems 227
D. The SCE Filter 231
E. Light-Adaptive Architecture 244
F. Wiring Complexity 256
IV. Spatio-Temporal Stability of Vision Chips 264
A. Introduction 264
B. Stability-Regularity 269
C. Explicit Stability Criteria 280
D. Transients 283
References 283
Algorithmic Techniques and Their Applications
Rudy Setiono
I. Introduction 287
II. Quasi-Newton Methods for Neural
Network Training 289
III. Selecting the Number of Output Units 295
IV. Determining the Number of Hidden Units 296
V. Selecting the Number of Input Units 303