Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/41565
Title: Video segmentation based on graphical models
Authors: Wang, Y.
Tan, T.
Loe, K.-F. 
Issue Date: 2003
Source: Wang, Y.,Tan, T.,Loe, K.-F. (2003). Video segmentation based on graphical models. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2 : II/335-II/342. ScholarBank@NUS Repository.
Abstract: This paper proposes a unified framework for spatio-temporal segmentation of video sequences. A Bayesian network is presented to model the interactions among the motion vector field, the intensity segmentation field, and the video segmentation field. The notions of distance transformation and Markov random field are used to express spatio-temporal constraints. Given consecutive frames, an optimization method is proposed to maximize the conditional probability density of the three fields in an iterative way. Experimental results show that the approach is robust and generates spatio-temporally coherent segmentation results.
Source Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/41565
ISSN: 10636919
Appears in Collections:Staff Publications

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