ACM Computing Surveys (CSUR)
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Histogram Thresholding using Beam Theory and Ambiguity Measures
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
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Feature space based approaches have been the most popular ones among those used to perform image segmentation. In this paper, a density modification framework is proposed in order to aid feature space based segmentation in images. The framework embeds a position-dependent property associated with each sample in the feature space of an image into the corresponding density map and hence modifies it. The property association and embedding operations in the framework is implemented using a fuzzy set theory based system devised with cue from beam theory of solid mechanics and the appropriateness of this approach is established. Experimental results of segmentation in images are given to demonstrate the effectiveness of the proposed framework.