10 April 2018 A robust fuzzy local Information c-means clustering algorithm with noise detection
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106151Z (2018) https://doi.org/10.1117/12.2302476
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
Abstract
Fuzzy c-means clustering (FCM), especially with spatial constraints (FCM_S), is an effective algorithm suitable for image segmentation. Its reliability contributes not only to the presentation of fuzziness for belongingness of every pixel but also to exploitation of spatial contextual information. But these algorithms still remain some problems when processing the image with noise, they are sensitive to the parameters which have to be tuned according to prior knowledge of the noise. In this paper, we propose a new FCM algorithm, combining the gray constraints and spatial constraints, called spatial and gray-level denoised fuzzy c-means (SGDFCM) algorithm. This new algorithm conquers the parameter disadvantages mentioned above by considering the possibility of noise of each pixel, which aims to improve the robustness and obtain more detail information. Furthermore, the possibility of noise can be calculated in advance, which means the algorithm is effective and efficient.
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Jiayu Shang, Jiayu Shang, Shiren Li, Shiren Li, Junwei Huang, Junwei Huang, } "A robust fuzzy local Information c-means clustering algorithm with noise detection", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106151Z (10 April 2018); doi: 10.1117/12.2302476; https://doi.org/10.1117/12.2302476
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