Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/104638
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dc.titleSupervised texture segmentation using the subspace mumford-shah model
dc.contributor.authorLaw, Y.N.
dc.contributor.authorLee, H.K.
dc.contributor.authorYip, A.M.
dc.date.accessioned2014-10-28T02:51:52Z
dc.date.available2014-10-28T02:51:52Z
dc.date.issued2009
dc.identifier.citationLaw, Y.N.,Lee, H.K.,Yip, A.M. (2009). Supervised texture segmentation using the subspace mumford-shah model. Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009 2 : 554-560. ScholarBank@NUS Repository.
dc.identifier.isbn9781601321190
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/104638
dc.description.abstractWe propose a novel image segmentation model, called the Subspace Mumford-Shah model, which incorporates subspace clustering techniques into a Mumford-Shah model to solve texture segmentation problems. The method first uses a supervised procedure to determine several optimal subspaces. These subspaces are then embedded into a Mumford-Shah objective function so that each segment of the optimal partition is homogeneous in its own subspace. The method outperforms standard Mumford-Shah models since it can separate textures which are less separated in the full feature space. The method also has an increased robustness and convergence speed compared to existing subspace clustering methods. Experimental results are presented to confirm the usefulness of subspace clustering in texture segmentation.
dc.sourceScopus
dc.subjectGabor transform
dc.subjectImage segmentation
dc.subjectMumford-Shah segmentation model
dc.subjectSubspace clustering
dc.subjectTexture segmentation
dc.typeConference Paper
dc.contributor.departmentMATHEMATICS
dc.description.sourcetitleProceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009
dc.description.volume2
dc.description.page554-560
dc.identifier.isiutNOT_IN_WOS
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