Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
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TL;DR
Pattern Recognition, Cluster Analysis for Object Data, Classifier Design, and Image Processing and Computer Vision are studied.
Abstract
Pattern Recognition 1 1. 1 Fuzzy models for pattern recognition 1 1.2Why fuzzy pattern recognition?7 1.3 Overview of the volume 1.4 Comments and bibliography 10 2 Cluster Analysis for Object Data 2.1 Cluster analysis 2.2 Batch point-prototype clustering models 14 A. The c-means models 16 B. Semi-supervised clustering models C. Probabilistic Clustering D. Remarks on HCM/FCM/PCM 34 E. The Reformulation Theorem 2.3 Non point-prototype clustering models A. The Gustafson-Kessel (GK) Model 41 B. Linear manifolds as prototypes C. Spherical Prototypes D. Elliptical Prototypes 54 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. Indirect measures for