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 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...
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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.
Series ForewordPreface1Pattern Recognition11.1Fuzzy models for pattern recognition11.2Why fuzzy pattern recognition?71.3Overview of the volume82Cluster Analysis for Object Data112.1Cluster analysis112.2Batch point-prototype clustering models142.3Non point-prototype clustering models392.4Cluster Validity872.5Feature Analysis1213Cluster Analysis for Relational Data1373.1Relational Data1373.2Object Data to Relational Data1463.3Hierarchical Methods1493.4Clustering by decomposition of fuzzy relations1533.5Relational clustering with objective functions1583.6Cluster validity for relational models1784Classifier Design1834.1Classifier design for object data1834.2Prototype classifiers1904.3Methods of prototype generation2014.4Nearest neighbor classifiers2414.5The Fuzzy Integral2534.6Fuzzy Rule-Based Classifiers2684.7Neural-like architectures for classification3704.8Adaptive resonance models4134.9Fusion techniques4424.10Syntactic pattern recognition4915Image Processing and Computer Vision5475.1Introduction5475.2Image Enhancement5505.3Edge Detection and Edge-Enhancement5625.4Edge Linking5725.5Segmentation5795.6Boundary Description and Surface Approximation6015.7Representation of Image Objects as Fuzzy Regions6245.8Spatial Relations6395.9Perceptual Grouping6515.10High-Level Vision658References cited in the text681References not cited in the text743App. 1 Acronyms and abbreviations753App. 2The Iris Data: Table I, Fisher (1936)759
