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Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.patcog.2005.02.006
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dc.titleA probabilistic approach for foreground and shadow segmentation in monocular image sequences
dc.contributor.authorWang, Y.
dc.contributor.authorTan, T.
dc.contributor.authorLoe, K.-F.
dc.contributor.authorWu, J.-K.
dc.date.accessioned2013-07-04T07:48:19Z
dc.date.available2013-07-04T07:48:19Z
dc.date.issued2005
dc.identifier.citationWang, Y., Tan, T., Loe, K.-F., Wu, J.-K. (2005). A probabilistic approach for foreground and shadow segmentation in monocular image sequences. Pattern Recognition 38 (11) : 1937-1946. ScholarBank@NUS Repository. https://doi.org/10.1016/j.patcog.2005.02.006
dc.identifier.issn00313203
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39733
dc.description.abstractThis paper presents a novel method of foreground and shadow segmentation in monocular indoor image sequences. The models of background, edge information, and shadow are set up and adaptively updated. A Bayesian network is proposed to describe the relationships among the segmentation label, background, intensity, and edge information. A maximum a posteriori - Markov random field estimation is used to boost the spatial connectivity of segmented regions. © 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.patcog.2005.02.006
dc.sourceScopus
dc.subjectBayesian network
dc.subjectForeground segmentation
dc.subjectGraphical model
dc.subjectMarkov random field
dc.subjectShadow detection
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1016/j.patcog.2005.02.006
dc.description.sourcetitlePattern Recognition
dc.description.volume38
dc.description.issue11
dc.description.page1937-1946
dc.description.codenPTNRA
dc.identifier.isiut000232113000012
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