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A non-iterative clustering based soft segmentation approach for a class of fuzzy images
Wang ZZ(王振洲); Yang YM(杨永明)
Department机器人学研究室
Source PublicationAPPLIED SOFT COMPUTING
ISSN1568-4946
2018-09-01
Volume70Pages:988-999
Indexed BySCI ; EI
EI Accession number20172103694499
WOS IDWOS:000443296000066
Contribution Rank1
KeywordClustering Slope Difference Distribution Interval Type-2 Fuzzy Logic Non-iterative Iterative
Abstract

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.

Language英语
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS KeywordEm Algorithm ; Incomplete Data ; Systems ; Likelihood ; Navigation
WOS Research AreaComputer Science
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/20494
Collection机器人学研究室
Corresponding AuthorWang ZZ(王振洲)
AffiliationState Key Lab of Robotics, Shenyang Institute of Automation, Chinese Academy of Science (CAS), Shenyang, China
Recommended Citation
GB/T 7714
Wang ZZ,Yang YM. A non-iterative clustering based soft segmentation approach for a class of fuzzy images[J]. APPLIED SOFT COMPUTING,2018,70:988-999.
APA Wang ZZ,&Yang YM.(2018).A non-iterative clustering based soft segmentation approach for a class of fuzzy images.APPLIED SOFT COMPUTING,70,988-999.
MLA Wang ZZ,et al."A non-iterative clustering based soft segmentation approach for a class of fuzzy images".APPLIED SOFT COMPUTING 70(2018):988-999.
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