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| 科研成果 |
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| 论文题目： |
A non-iterative clustering based soft segmentation approach for a class of fuzzy images |
| 第一作者： |
Wang ZZ(王振洲);Yang YM(杨永明) |
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| 发表刊物： |
Applied Soft Computing Journal |
| 发表年度： |
2017 |
| 卷,期,页： |
,, |
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| 论文摘要： |
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. |
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