Image segmentation algorithm based on neutrosophic fuzzy clustering with non‐local information
| Autor: | Qing Gao, Yuqi Li, Jinyu Wen, Shibin Xuan, Qihui Peng |
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| Rok vydání: | 2020 |
| Předmět: |
Fuzzy clustering
Mathematics::General Mathematics Computer science business.industry Fuzzy set ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Boundary (topology) 020206 networking & telecommunications Pattern recognition 02 engineering and technology Image segmentation Fuzzy logic ComputingMethodologies_PATTERNRECOGNITION Computer Science::Computer Vision and Pattern Recognition Signal Processing 0202 electrical engineering electronic engineering information engineering Image segmentation algorithm 020201 artificial intelligence & image processing Segmentation Computer Vision and Pattern Recognition Artificial intelligence Electrical and Electronic Engineering Cluster analysis business Software |
| Zdroj: | IET Image Processing. 14:576-584 |
| ISSN: | 1751-9667 |
| DOI: | 10.1049/iet-ipr.2018.5949 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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