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Image Guided Fuzzy C-Means for Image Segmentation
Guo L.; Chen L.; Wu Y.; Chen C.L.P.
Source PublicationInternational Journal of Fuzzy Systems
ISSN21993211 15622479

Prior knowledge has been considered as valuable supplementary information in many image processing techniques. In this paper, we take the input image itself as the guidance prior and develop a novel fuzzy clustering algorithm to segment it by adding a new term to the objective function of Fuzzy C-Means. The new term comes from Guided Filter for its capability in noise suppression and edge-preserving smoothing. As a result, the memberships derived from the new objective function incorporate the guidance information from the image to be segmented. In this way, the segmentation result retains more subtle details on the boundaries of segments. According to experimental results, the proposed method shows good performance in image segmentation tasks especially for images with high noise rates.

KeywordEdge Information Fuzzy Clustering Method Guided Filter Prior Knowledge
URLView the original
Indexed BySCI
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence
WOS IDWOS:000417107100002
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Citation statistics
Cited Times [WOS]:7   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Guo L.,Chen L.,Wu Y.,et al. Image Guided Fuzzy C-Means for Image Segmentation[J]. International Journal of Fuzzy Systems,2017,19(6):1660-1669.
APA Guo L.,Chen L.,Wu Y.,&Chen C.L.P..(2017).Image Guided Fuzzy C-Means for Image Segmentation.International Journal of Fuzzy Systems,19(6),1660-1669.
MLA Guo L.,et al."Image Guided Fuzzy C-Means for Image Segmentation".International Journal of Fuzzy Systems 19.6(2017):1660-1669.
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