Image Processing and Pattern Recognition

Frank Y. Shih9780470404614, 0470404612

A comprehensive guide to the essential principles of image processing and pattern recognition
Techniques and applications in the areas of image processing and pattern recognition are growing at an unprecedented rate. Containing the latest state-of-the-art developments in the field, Image Processing and Pattern Recognition presents clear explanations of the fundamentals as well as the most recent applications. It explains the essential principles so readers will not only be able to easily implement the algorithms and techniques, but also lead themselves to discover new problems and applications.
Unlike other books on the subject, this volume presents numerous fundamental and advanced image processing algorithms and pattern recognition techniques to illustrate the framework. Scores of graphs and examples, technical assistance, and practical tools illustrate the basic principles and help simplify the problems, allowing students as well as professionals to easily grasp even complicated theories. It also features unique coverage of the most interesting developments and updated techniques, such as image watermarking, digital steganography, document processing and classification, solar image processing and event classification, 3-D Euclidean distance transformation, shortest path planning, soft morphology, recursive morphology, regulated morphology, and sweep morphology. Additional topics include enhancement and segmentation techniques, active learning, feature extraction, neural networks, and fuzzy logic.
Featuring supplemental materials for instructors and students, Image Processing and Pattern Recognition is designed for undergraduate seniors and graduate students, engineering and scientific researchers, and professionals who work in signal processing, image processing, pattern recognition, information security, document processing, multimedia systems, and solar physics.

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Table of contents :
Front Cover……Page 1
Image Processing and Pattern Recognition……Page 4
Copyright Page……Page 5
Contents……Page 6
Contributors……Page 14
Preface……Page 16
I. Introduction……Page 22
II. Pattern Recognition Problem……Page 24
III. Neural Networks in Feature Extraction……Page 32
IV. Classification Methods: Statistical and Neural……Page 41
V. Neural Network Applications in Pattern Recognition……Page 59
VI. Summary……Page 73
References……Page 74
I. Introduction……Page 82
II. Applications……Page 84
III. Data Acquisition and Preprocessing……Page 85
IV. Statistical Classifiers……Page 86
V. Neural Classifiers……Page 95
VI. Literature Survey……Page 100
VII. Simulation Results……Page 102
VIII. Conclusions……Page 106
References……Page 107
I. Introduction……Page 110
II. Review of Artificial Neural Network Applications in Medical Imaging……Page 116
III. Segmentation of Arteriograms……Page 120
IV. Back-Propagation Artificial Neural Network for Arteriogram Segmentation: A Supervised Approach……Page 122
V. Self-Adaptive Artificial Neural Network for Arteriogram Segmentation: An Unsupervised Approach……Page 128
VI. Conclusions……Page 145
References……Page 150
I. Introduction……Page 154
II. Small-Size Neuro-Recognition Technique Using the Masks……Page 155
III. Mask Determination Using the Genetic Algorithm……Page 164
IV. Development of the Neuro-Recognition Board Using the Digital Signal Processor……Page 173
V. Unification of Three Core Techniques……Page 177
VI. Conclusions……Page 179
References……Page 180
I. Introduction……Page 182
II. Classification Paradigms……Page 185
III. Neural Network Classifiers……Page 188
IV. Classification Reliability……Page 193
V. Evaluating Neural Network Classification Reliability……Page 195
VI. Finding a Reject Rule……Page 199
VII. Experimental Results……Page 206
VIII. Summary……Page 217
References……Page 218
I. Introduction……Page 222
II. Physiological Background……Page 223
III. Regularization Vision Chips……Page 242
IV. Spatio-Temporal Stability of Vision Chips……Page 285
References……Page 304
I. Introduction……Page 308
II. Quasi-Newton Methods for Neural Network Training……Page 310
III. Selecting the Number of Output Units……Page 316
IV. Determining the Number of Hidden Units……Page 317
V. Selecting the Number of Input Units……Page 324
VI. Determining the Network Connections by Pruning……Page 330
VII. Applications of Neural Networks to Data Mining……Page 334
VIII. Summary……Page 337
References……Page 338
I. Introduction……Page 342
II. Adaptive Learning Algorithm……Page 345
III. Simulation Results……Page 356
IV. Applications……Page 364
V. Conclusion……Page 370
VI. Appendix……Page 371
References……Page 372
I. Introduction……Page 374
II. Complexity Regularization……Page 378
III. Sensitivity Calculation……Page 383
IV. Optimization through Constraint Satisfaction……Page 389
V. Local and Distributed Bottlenecks……Page 393
VI. Interactive Pruning……Page 395
VII. Other Pruning Methods……Page 397
References……Page 399
Index……Page 404