Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Springer Science & Business Media, Sep 28, 2006 - Computers - 776 pages
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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aggregation applied approach approximation ARTl Bezdek c-means Choquet integral classifier design cluster validity clustering algorithms computed computer vision crisp label data set decision tree defined defuzzification denote discussed distance edge detection edge image equation error rate estimate Euclidean Euclidean distance Euclidean norm example extraction FCM-AO feature vectors filter firing strength fusion fuzzy clustering fuzzy decision tree fuzzy integral fuzzy models fuzzy rules fuzzy sets fuzzy systems gradient graph gray level Hough transform i-th IEEE image processing initial input Iris iteration Keller Krishnapuram label vectors linear linguistic low low matrix measure membership functions membership values method neural networks node norm number of clusters objective function optimization output pair parameters pattern recognition pixel point prototypes possibilistic primitives problem represent rule-based Section segmentation shell clustering shown in Figure shows spatial relations structure subsets T-norm Table termination threshold training data update variables weights