A robust approach to segment desired object based on salient colors
Journal on Image and Video Processing - Color in Image and Video Processing
Region-level motion-based foreground segmentation under a Bayesian network
IEEE Transactions on Circuits and Systems for Video Technology
Depth-Spatio-Temporal Joint Region-of-Interest Extraction and Tracking for 3D Video
FGIT '09 Proceedings of the 1st International Conference on Future Generation Information Technology
IEEE Transactions on Circuits and Systems for Video Technology
A Bayesian network for foreground segmentation in region level
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Depth perceptual region-of-interest based multiview video coding
Journal of Visual Communication and Image Representation
Multivariate image segmentation using semantic region growing with adaptive edge penalty
IEEE Transactions on Image Processing
Video-object segmentation and 3D-trajectory estimation for monocular video sequences
Image and Vision Computing
Stereo-based object segmentation combining spatio-temporal information
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Journal of Visual Communication and Image Representation
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This paper proposes a probabilistic framework for spatiotemporal segmentation of video sequences. Motion information, boundary information from intensity segmentation, and spatial connectivity of segmentation are unified in the video segmentation process by means of graphical models. A Bayesian network is presented to model interactions among the motion vector field, the intensity segmentation field, and the video segmentation field. The notion of the Markov random field is used to encourage the formation of continuous regions. Given consecutive frames, the conditional joint probability density of the three fields is maximized in an iterative way. To effectively utilize boundary information from the intensity segmentation, distance transformation is employed in local objective functions. Experimental results show that the method is robust and generates spatiotemporally coherent segmentation results. Moreover, the proposed video segmentation approach can be viewed as the compromise of previous motion based approaches and region merging approaches.