NDLI: Image Segmentation Using Thresholding by Local Fuzzy Entropy-Based Competitive Fuzzy Edge Detection

Content Provider	IEEE Xplore Digital Library
Author	Bourjandi, M.
Copyright Year	2009
Abstract	In this paper, we present a new thresholding approach by local fuzzy entropy based competitive fuzzy edge detection for image segmentation which assign appropriate threshold effectively and reduces the affects of noise in edge detection and segmentation. In this algorithm first, edges detected by fuzzy logic and competitive rules, then there would be improvement in quality obtained edges by fuzzy entropy. The end by the information of received edges suitable threshold fined for image segmentation and then we will segment the images properly. The in novation, of this paper is the improvement in the edges of image in competitive fuzzy edge detection which it would be usable in the image segmentation. The results show that the quality of segmentation which is based on the suggested approach for the white Gaussian noise images is better than local entropy algorithm.
Starting Page	298
Ending Page	301
File Size	557251
Page Count	4
File Format	PDF
ISBN	9781424453658
DOI	10.1109/ICCEE.2009.172
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-28
Publisher Place	UAE
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	thresholding Image edge detection Image processing Noise reduction Entropy fuzzy edge Fuzzy logic Image segmentation Gaussian noise Machine vision fuzzy entropy segmentation competitive Colored noise Pixel
Content Type	Text
Resource Type	Article

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