文章基本信息

标题：Human Object Extraction Using Nonextensive Fuzzy Entropy and Chaos Differential Evolution
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作者：Fangyan Nie ; Jianqi Li ; Qiusheng Rong 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：2013
卷号：6
期号：2
出版社：SERSC
摘要：Human object extraction from infrared image has broad applications, and has become an active research area in image processing community. Combined with chaos differential evolution (CDE) algorithm and morphological operators, a novel infrared human target extraction method is proposed based on nonextensive fuzzy entropy. Firstly, the image was transformed into a fuzzy domain by fuzzy membership function, and the image nonextensive fuzzy entropy was constructed. Then, the image was segmented by thresholding based on the maximum entropy principle and the pseudoadditivity rule of nonextensive entropy. In order to reduce the search time of optimal threshold selection, the CDE algorithm was presented. Finally, the object was extracted using morphological operators to denoise, fill cavity on the threshold segmented image. Experimental results show that the proposed method is efficient and requires less computation time.
关键词：Infrared image processing; Human object extraction; Nonextensive fuzzy;entropy; Chaos differential evolution; Morphological operator