Article ID: | iaor20119612 |
Volume: | 62 |
Issue: | 7 |
Start Page Number: | 2824 |
End Page Number: | 2833 |
Publication Date: | Oct 2011 |
Journal: | Computers and Mathematics with Applications |
Authors: | Xu Kai, Qin Kun, Liu Feilong, Li Deyi |
Keywords: | datamining |
Both the cloud model and type‐2 fuzzy sets deal with the uncertainty of membership which traditional type‐1 fuzzy sets do not consider. Type‐2 fuzzy sets consider the fuzziness of the membership degrees. The cloud model considers fuzziness, randomness, and the association between them. Based on the cloud model, the paper proposes an image segmentation approach which considers the fuzziness and randomness in histogram analysis. For the proposed method, first, the image histogram is generated. Second, the histogram is transformed into discrete concepts expressed by cloud models. Finally, the image is segmented into corresponding regions based on these cloud models. Segmentation experiments by images with bimodal and multimodal histograms are used to compare the proposed method with some related segmentation methods, including Otsu threshold, type‐2 fuzzy threshold, fuzzy