Fuzzy Models and Algorithms for Pattern Recognition and Image Processing

By: James Keller, Raghu Krisnapuram, James C. Bezdek

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Hardcover | 31 August 1999

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796 Pages

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24.77 x 17.15 x 4.45

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Fuzzy Models and Algorithms for Pattern Recognition and Image Processing presents a comprehensive introduction of the use of fuzzy models in pattern recognition and selected topics in image processing and computer vision. Unique to this volume in the Kluwer Handbooks of Fuzzy Sets Series is the fact that this book was written in its entirety by its four authors. A single notation, presentation style, and purpose are used throughout. The result is an extensive unified treatment of many fuzzy models for pattern recognition. The main topics are clustering and classifier design, with extensive material on feature analysis relational clustering, image processing and computer vision. Also included are numerous figures, images and numerical examples that illustrate the use of various models involving applications in medicine, character and word recognition, remote sensing, military image analysis, and industrial engineering.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Computer Vision Mathematics Applied Mathematics Fuzzy Set Theory Engineering & Technology Other Technologies & Applied Sciences Applied Optics Energy Technology & Engineering

Electrical Engineering Electronics & Communications Engineering Electronics Engineering Automatic Control Engineering

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Paperback

Published: 25th March 2005

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Series Foreword
Preface
Pattern Recognition	p. 1
Fuzzy models for pattern recognition	p. 1
Why fuzzy pattern recognition?	p. 7
Overview of the volume	p. 8
Cluster Analysis for Object Data	p. 11
Cluster analysis	p. 11
Batch point-prototype clustering models	p. 14
Non point-prototype clustering models	p. 39
Cluster Validity	p. 87
Feature Analysis	p. 121
Cluster Analysis for Relational Data	p. 137
Relational Data	p. 137
Object Data to Relational Data	p. 146
Hierarchical Methods	p. 149
Clustering by decomposition of fuzzy relations	p. 153
Relational clustering with objective functions	p. 158
Cluster validity for relational models	p. 178
Classifier Design	p. 183
Classifier design for object data	p. 183
Prototype classifiers	p. 190
Methods of prototype generation	p. 201
Nearest neighbor classifiers	p. 241
The Fuzzy Integral	p. 253
Fuzzy Rule-Based Classifiers	p. 268
Neural-like architectures for classification	p. 370
Adaptive resonance models	p. 413
Fusion techniques	p. 442
Syntactic pattern recognition	p. 491
Image Processing and Computer Vision	p. 547
Introduction	p. 547
Image Enhancement	p. 550
Edge Detection and Edge-Enhancement	p. 562
Edge Linking	p. 572
Segmentation	p. 579
Boundary Description and Surface Approximation	p. 601
Representation of Image Objects as Fuzzy Regions	p. 624
Spatial Relations	p. 639
Perceptual Grouping	p. 651
High-Level Vision	p. 658
References cited in the text	p. 681
References not cited in the text	p. 743
App. 1 Acronyms and abbreviations	p. 753
The Iris Data: Table I, Fisher (1936)	p. 759
Table of Contents provided by Blackwell. All Rights Reserved.

Fuzzy Models and Algorithms for Pattern Recognition and Image Processing

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Hardcover

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