Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/39535
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dc.titleMulticue MRF image segmentation: Combining texture and color features
dc.contributor.authorKato, Z.
dc.contributor.authorPong, T.-C.
dc.contributor.authorQiang, S.G.
dc.date.accessioned2013-07-04T07:43:47Z
dc.date.available2013-07-04T07:43:47Z
dc.date.issued2002
dc.identifier.citationKato, Z.,Pong, T.-C.,Qiang, S.G. (2002). Multicue MRF image segmentation: Combining texture and color features. Proceedings - International Conference on Pattern Recognition 16 (1) : 660-663. ScholarBank@NUS Repository.
dc.identifier.issn10514651
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39535
dc.description.abstractHerein, we propose a new Markov random field (MRF) image segmentation model which aims at combining color and texture features. The model has a multi-layer structure: Each feature has its own layer, called feature layer, where an MRF model is defined using only the corresponding feature. A special layer is assigned to the combined MRF model. This layer interacts with each feature layer and provides the segmentation based on the combination of different features. The uniqueness of our algorithm is that it provides both color only and texture only segmentations as well as a segmentation based on combined color and texture features. The number of classes on feature layers is given by the user but it is estimated on the combined layer. © 2002 IEEE.
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleProceedings - International Conference on Pattern Recognition
dc.description.volume16
dc.description.issue1
dc.description.page660-663
dc.description.codenPICRE
dc.identifier.isiutNOT_IN_WOS
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