| Autor: |
Jinyu Wen, Shibin Xuan, Yuqi Li, Qihui Peng, Qing Gao |
| Předmět: |
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| Zdroj: |
IET Image Processing (Wiley-Blackwell); 2020, Vol. 14 Issue 3, p576-584, 9p |
| Abstrakt: |
To improve the boundary processing ability and anti-noise performance of image segmentation algorithm, a neutrosophic fuzzy clustering algorithm based on non-local information is proposed here. Initially, the proposed approach uses the data distribution of deterministic subset to determine the clustering centre of the fuzzy subset. Besides, the fuzzy non-local pixel correlation is introduced into the neutrosophic fuzzy mean clustering algorithm. The experimental results on synthetic images, medical images and natural images show that the proposed method is more robust and more accurate than the existing clustering segmentation methods. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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