| A non-iterative clustering based soft segmentation approach for a class of fuzzy images | |
| Wang, Zhenzhou; Yang, Yongming | |
| Corresponding Author | Wang, Zhenzhou(wangzhenzhou@sia.cn) |
| 2018-09-01 | |
| Source Publication | APPLIED SOFT COMPUTING
 |
| ISSN | 1568-4946 |
| Volume | 70Pages:988-999 |
| 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. |
| Keyword | Clustering Slope difference distribution Interval type-2 fuzzy logic Non-iterative Iterative |
| DOI | 10.1016/j.asoc.2017.05.025 |
| Indexed By | SCI |
| Language | 英语 |
| WOS Research Area | Computer Science |
| WOS Subject | Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications |
| WOS ID | WOS:000443296000066 |
| Publisher | ELSEVIER SCIENCE BV |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.imr.ac.cn/handle/321006/129401 |
| Collection | 中国科学院金属研究所 |
| Corresponding Author | Wang, Zhenzhou |
| Affiliation | Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, Shenyang, Liaoning, Peoples R China |
| Recommended Citation 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. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment