Rough-Fuzzy Granulation, Rough Entropy and Image Segmentation

Abstract

This talk has two parts. The first part describes how the concept of rough-fuzzy granulation can be used for the problem of case generation, with varying reduced number of features, in a case based reasoning system, and the application to multi-spectral image segmentation. Here the synergistic integration of EM algorithm, minimal spanning tree and granular computing for efficient segmentation is described. The second part deals with defining a new definition of image entropy in a rough set theoretic framework, and its application to the object extraction problem from images by minimizing both object and background roughness. Granules carry local information and reflect the inherent spatial relation of the image by treating pixels of a window as indiscernible or homogeneous. Maximization of homogeneity in both object and background regions during their partitioning is achieved through maximization of rough entropy; thereby providing optimum results for object background classification. The effect of granule size is also discussed.