文章基本信息

标题：Study on Improved Algorithm for Image Edge Detection Based on Genetic Fuzzy
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作者：Xiaoguang Li ; Bianxia Wu ; Yuanbo Li 等
期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2016
卷号：9
期号：7
页码：329-340
DOI：10.14257/ijsip.2016.9.7.29
出版社：SERSC
摘要：Aiming at the existing edge detection algorithm of edge vague, the pseudo-edge cannot be removed and algorithm results do not achieve optimal results by virtue. In order to improve the reliability and effectiveness of edge detection, the proposed optimization tool template coefficient method, to design the coding, Sobel filter and fitness function of genetic fuzzy clustering algorithm. Through interpolating, smooth handling and filtering with the updated active contour model. Based on the traditional edge detection algorithm is analyzed, combined with fuzzy membership functions and genetic operators for edge detection algorithm was improved by genetic fuzzy clustering. Through the simulation results showed that this new algorithm was feasible. Theoretical analysis and experimental results demonstrate that, the new algorithm in this paper is highly antinoise and able to get better image edges.
关键词：Image Processing; Genetic Algorithms; Fuzzy Clustering; Edge Detection; ; Fuzzy Membership Functions