Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.patcog.2005.02.006
Title: A probabilistic approach for foreground and shadow segmentation in monocular image sequences
Authors: Wang, Y.
Tan, T.
Loe, K.-F. 
Wu, J.-K.
Keywords: Bayesian network
Foreground segmentation
Graphical model
Markov random field
Shadow detection
Issue Date: 2005
Source: Wang, 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
Abstract: This 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.
Source Title: Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/39733
ISSN: 00313203
DOI: 10.1016/j.patcog.2005.02.006
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