NDLI: Image Segmentation using Global and Local Fuzzy Statistics

Content Provider	IEEE Xplore Digital Library
Author	Sen, D. Pal, S.K.
Copyright Year	2006
Description	Author affiliation: Center for Soft Comput. Res., Indian Stat. Inst., Calcutta (Sen, D.; Pal, S.K.)
Abstract	In this paper, criterion optimization based image thresholding techniques to perform segmentation using global and local fuzzy statistics are presented. The global and local fuzzy statistics considered for an image are the fuzzy histogram and fuzzy co-occurrence matrix of the image, respectively. A novel way of adapting the membership function, which is required to calculate the fuzzy statistics, to the local nature of the corresponding crisp first-order statistic (histogram) is suggested. The fuzzy statistics of an image obtained using such an adaptive membership function are called adaptive fuzzy statistics. Experimental results of various image segmentation techniques using crisp, fuzzy and the proposed adaptive fuzzy statistics are given. A comparative study demonstrating the usefulness of fuzzy statistics in image segmentation and the effectiveness of adapting the membership function in order to determine the fuzzy statistics is presented
Starting Page	1
Ending Page	6
File Size	7428345
Page Count	6
File Format	PDF
ISBN	1424403693
DOI	10.1109/INDCON.2006.302813
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-09-15
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer vision Histogram Digital images Image processing Computational modeling Co-occurrence matrix Statistics Image segmentation Histograms Image analysis Member-ship function Fuzzy co-occurrence matrix Layout Pixel Fuzzy histogram
Content Type	Text
Resource Type	Article

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