摘要
采用模糊集中的包含度提出阈值化分割图像方法.由于人眼视觉的主观性和图像结构的不确定性使得图像分割比较适合采用模糊技术进行处理.首先引入基于模糊集的包含度理论,其次基于模糊包含度公式定义图像分割选取阈值的新准则函数,最后基于互信息量和混沌理论给出该分割方法中的模糊隶属函数参数的最佳选取办法.实验结果表明,本文方法是可行的,且分割性能明显优于基于模糊熵或相似度的分割法.
In this paper, an image segmentation method based on subsethood measure theory of fuzzy set is proposed. Because of the subjective of human vision and the uncertainty of image structure, fuzzy techniques are used in image segmentation. Firstly, the subsethood measure theory of fuzzy set is introduced. Then the new criteria function of image segmentation threshold choosing is defined on the basis of fuzzy subsethood theory. Finally, the optimal method for choosing parameters of fuzzy membership is presented based on mutual information and Chaos theory. Experimental results show that the image thresholding segmentation method based on subsethood measure theory is feasible, and its segmentation performance is obviously better than that of thresholding method based on fuzzy entropy or similarity measure.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2007年第6期805-814,共10页
Pattern Recognition and Artificial Intelligence
基金
陕西省教育厅计划资助项目(No.06JK194)
关键词
图像分割
阈值法
模糊熵
相似度
包含度
隶属函数
Image Segmentation, Thresholding Method, Fuzzy Entropy, Similarity Measure, Subsethood Measure, Membership Function
