Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Springer Science & Business Media, Aug 31, 1999 - 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 ART1 Bezdek c-means Choquet integral classifier design cluster validity clustering algorithm computed crisp label crisp partition data set decision tree defined defuzzification denote density discussed distance edge edge detection equation error rate estimate Euclidean Euclidean distance Euclidean norm example FCM-AO feature vectors filter firing strength FLVQ FOSART fusion fuzzy clustering fuzzy decision tree fuzzy integral fuzzy models fuzzy neurons fuzzy rules fuzzy sets fuzzy systems graph Hough transform i-th initial input iteration Keller Krishnapuram label vectors learning rates linear linguistic matrix measure membership functions membership values method neural networks neuron norm number of clusters objective function optimal output pair parameters pattern recognition pixel PMFs point prototypes possibilistic primitives problem produce regions represented rule-based Section segmentation shown in Figure shows SOFM string structure subsets Sugeno T-norm Table termination training data update variables weights
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