A non-iterative clustering based soft segmentation approach for a class of fuzzy images

doi:10.1016/j.asoc.2017.05.025

IMR OpenIR

	A non-iterative clustering based soft segmentation approach for a class of fuzzy images
	Wang, Zhenzhou; Yang, Yongming
通讯作者	Wang, Zhenzhou(wangzhenzhou@sia.cn)
	2018-09-01
发表期刊	APPLIED SOFT COMPUTING
ISSN	1568-4946
卷号	70 页码:988-999
摘要	Many machine vision applications require to compute the size of the fuzzy object in the captured image sequences robustly. The size variation with the change of time is then utilized for the different purposes, e.g. data analysis, diagnosis and feedback control. To this end, robust image segmentation is required in the first place. Many state-of-the-art segmentation methods are based on iterative clustering, e.g. the expectation maximization (EM) method, the K-means method and the fuzzy C-means method. One drawback of the iterative learning based clustering methods is that they perform poorly when there are severe noise or outliers. Consequently, the hard segmentation results for the fuzzy images by these segmentation results are not robust enough and the computed sizes based on the hard segmentation results are not accurate either. In this paper, we propose a non-iterative clustering based approach to segment the fuzzy object from the fuzzy images. Instead of yielding a hard segmentation result, we utilize interval type-2 fuzzy logic to assign membership to the final segmentation result. Accordingly, we compute the size of the object based on the soft segmentation result. Experimental results show that the proposed non-iterative soft segmentation approach is more robust in computing the size of the fuzzy object than the hard approaches that yield a distinct segmentation result. (C) 2017 The Authors. Published by Elsevier B.V.
关键词	Clustering Slope difference distribution Interval type-2 fuzzy logic Non-iterative Iterative
DOI	10.1016/j.asoc.2017.05.025
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:000443296000066
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/129396
专题	中国科学院金属研究所
通讯作者	Wang, Zhenzhou
作者单位	Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, Shenyang, Liaoning, Peoples R China
推荐引用方式 GB/T 7714	Wang, Zhenzhou,Yang, Yongming. A non-iterative clustering based soft segmentation approach for a class of fuzzy images[J]. APPLIED SOFT COMPUTING,2018,70:988-999.
APA	Wang, Zhenzhou,&Yang, Yongming.(2018).A non-iterative clustering based soft segmentation approach for a class of fuzzy images.APPLIED SOFT COMPUTING,70,988-999.
MLA	Wang, Zhenzhou,et al."A non-iterative clustering based soft segmentation approach for a class of fuzzy images".APPLIED SOFT COMPUTING 70(2018):988-999.