Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
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 Kluw
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THE HANDBOOKS OF FUZZY SETS SERIES Series Editors Didier Dubois and Henri Prade IR/T, Universite Paul Sabatier, Toulouse, France
FUNDAMENTALS OF FUZZY SETS, edited by Didier Dubois and Henri Prade MATHEMATICS OF FUZZY SETS: Logic, Topology, and Measure Theory, edited by Ulrich Hohle and Stephen Ernest Rodabaugh FUZZY SETS IN APPROXIMATE REASONING AND INFORMATION SYSTEMS, edited by James C. Bezdek, Didier Dubois and Henri Prade FUZZY MODELS AND ALGORITHMS FOR PATTERN RECOGNITION AND IMAGE PROCESSING, by James C. Bezdek, James Keller, Raghu Krisnapuram and Nikhil R. Pal FUZZY SETS IN DECISION ANALYSIS, OPERATIONS RESEARCH AND STATISTICS, edited by Roman Slowinski FUZZY SYSTEMS: Modeling and Control, edited by Hung T. Nguyen and Michio Sugeno PRACTICAL APPLICATIONS OF FUZZY TECHNOLOGIES, edited by HansJiirgen Zimmermann
FUZZY MODELS AND ALGORITHMS FOR PATTERN RECOGNITION AND IMAGE PROCESSING
James C. Bezdek University of West Florida
James Keller University ofMissouri
Raghu Krisnapuram Colorado School ofMines
Nikhil R. Pal Indian Statistical Institute
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Springer Science+Business Media, LLC
Library of Congress Cataloging-in-Publication Data Fuzzy models and algorithms for pattern recognition and image processing I James C. Bezdek ... [et al.]. p. em. -- (Handbooks of fuzzy sets series ; FSHS 4) Includes bibliographical references and index. ISBN 978-0-387-24515-7 ISBN 978-0-387-24579-9 (eBook) DOI 10.1007/978-0-387-24579-9 I. Optical pattern recognition. 2. Fuzzy algorithms. 3. Cluster analysis. 4. Image processing. 5. Computer vision. I. Bezdek, James C., 1939II. Series. TA1650.F89 1999 006 .4'2--dc21 99-27835 CIP Copyright © 1999 by Springer Science+Business Media New York Originally published by Kluwer Academic Publishers in 1999 Softcover reprint of the hardcover 1st edition 1999 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher, Springer Science+Business Media, LLC Printed on acid-free paper.
Contents
Series Foreword
v
Preface
vii
1 Pattern Recognition 1. 1 Fuzzy models for pattern recognition 1.2 Why fuzzy pattern recognition? 1.3 Overview of the volume 1.4 Comments and bibliography
1 1 7 8 10
2 Cluster Analysis for Object Data 2.1 Cluster analysis 2.2 Batch point-prototype clustering models A. The c-means models B. Semi-supervised clustering models C. Probabilistic Clustering D. Remarks on HCM/FCM/PCM E. The Reformulation Theorem 2.3 Non point-prototype clustering models A. The Gustafson-Kessel (GK) Model B. Linear manifolds as prototypes C. Spherical Prototypes D. Elliptical Prototypes E. Quadric Prototypes F. Norm induced shell prototypes G. Regression models as prototypes H. Clustering for robust parametric estimation 2.4 Cluster Validity A. Direct Measures B. Davies-Bouldin Index C. Dunn's index D. Indirect measures for fuzzy clusters E. Standardizing and normalizing indirect indices F. Indi
