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http://dx.doi.org/10.5391/JKIIS.2010.20.3.368

Space Partition using Context Fuzzy c-Means Algorithm for Image Segmentation  

Roh, Seok-Beom (대전대학교 컴퓨터공학과)
Ahn, Tae-Chon (원광대학교 전자 및 제어공학부)
Baek, Yong-Sun (대덕대학 컴퓨터웹정보과)
Kim, Yong-Soo (대전대학교 컴퓨터공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.20, no.3, 2010 , pp. 368-374 More about this Journal
Abstract
Image segmentation is the basic step in the field of the image processing for pattern recognition, environment recognition, and context analysis. The Otsu's automatic threshold selection, which determines the optimal threshold value to maximize the between class scatter using the distribution information of the normalized histogram of a image, is the famous method among the various image segmentation methods. For the automatic threshold selection proposed by Otsu, it is difficult to determine the optimal threshold value by considering the sub-region characteristic of the image because the Otsu's algorithm analyzes the global histogram of a image. In this paper, to alleviate this difficulty of Otsu's image segmentation algorithm and to improve image segmentation capability, the original image is divided into several sub-images by using context fuzzy c-means algorithm. The proposed fuzzy Otsu threshold algorithm is applied to the divided sub-images and the several threshold values are obtained.
Keywords
Image segmentation; Otsu threshold method; Context fuzzy c-means algorithm; Space partition;
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