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.
Author(s): Michal Baczynski, Balasubramaniam Jayaram
Series: The Handbooks of Fuzzy Sets
Edition: 1
Publisher: Springer
Year: 2005
Language: English
Pages: 785
THE HANDBOOKS OF FUZZY SETS SERIES......Page 2
FUZZY MODELS AND ALGORITHMS FOR PATTERN RECOGNITION AND IMAGE PROCESSING......Page 3
Contents......Page 5
Preface......Page 10
1.1 Fuzzy models for pattern recognition......Page 15
1.2 Why fuzzy pattern recognition?......Page 21
1.3 Overview of the volume......Page 22
1.4 Comments and bibliography......Page 24
2.1 Cluster analysis......Page 25
2.2 Batch point-prototype clustering models......Page 28
2.3 Non point-prototype clustering models......Page 53
2.4 Cluster Validity......Page 101
2.5 Feature Analysis......Page 135
2.6 Comments and bibliography......Page 144
3.1 Relational Data......Page 151
3.2 Object Data to Relational Data......Page 160
3.3 Hierarchical Methods......Page 163
3.4 Clustering by decomposition of fuzzy relations......Page 167
3.5 Relational clustering with objective functions......Page 172
3.6 Cluster validity for relational models......Page 192
3.7 Comments and bibliography......Page 194
4.1 Classifier design for object data......Page 197
4.2 Prototype classifiers......Page 204
4.3 Methods of prototype generation......Page 215
4.4 Nearest nei^bor classifiers......Page 255
4.5 The Fuzzy Integral......Page 267
4.6 Fuzzy Rule-Based Classifiers......Page 282
4.7 Neural-like architectiures for classification......Page 384
4.8 Adaptive resonance models......Page 427
4.9 Fusion techniques......Page 456
4.10 Syntactic pattern recognition......Page 505
4.11 Comments and bibliography......Page 537
5.1 Introduction......Page 561
5.2 Image Enhancement......Page 564
5.3 Edge Detection and Edge Enhancement......Page 576
5.4 Edge Unking......Page 586
5.5 Segmentation......Page 593
5.6 Boundary Description and Smface Approximation......Page 615
5.7 Representation of Image Objects as Fuzzy Regions......Page 638
5.8 Spatial Relations......Page 653
5.9 Perceptual Grouping......Page 665
5.10 High-Level Vision......Page 672
5.11 Comments and bibliography......Page 677
Epilogue......Page 692
References cited in the text......Page 693
Appendix 1 Acronyms and abbreviations......Page 764
i)4>pendix 2 The Iris Data: Table I, Fisher (1936)......Page 769
Index......Page 770
