Fuzzy Pattern Recognition



Classical models of pattern recognition partition a set of patterns into classes depending on the similarity in features of the patterns. When the distinctive features of the patterns are correctly identified, the classes can easily be distinguished in the feature space. Unfortunately, features in most pattern recognition problems are selected on an ad hoc basis, consequently causing the pattern classes to overlap, thereby leading to an ambiguity in object recognition. This chapter presents a well-known technique for fuzzy pattern recognition, capable of partitioning the patterns by soft boundaries. Thus a pattern may be classified into one or more classes with a certain degree of membership to belong to each class. The algorithm for fuzzy pattern recognition is numerically illustrated, and its application in object recognition from real time video frames is also presented.


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