A probabilistic approach for foreground and shadow segmentation in monocular image sequences

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摘要

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.

论文关键词：Bayesian network,Foreground segmentation,Graphical model,Markov random field,Shadow detection

论文评审过程：Received 2 October 2003, Revised 17 February 2005, Accepted 17 February 2005, Available online 20 April 2005.

论文官网地址：https://doi.org/10.1016/j.patcog.2005.02.006