IEEE Computer Graphics and Applications
Sparse matrix solvers on the GPU: conjugate gradients and multigrid
ACM SIGGRAPH 2003 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Bilayer Segmentation of Live Video
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Robust Tracking and Stereo Matching under Variable Illumination
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Better Foreground Segmentation for Static Cameras via New Energy Form and Dynamic Graph-cut
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Soft scissors: an interactive tool for realtime high quality matting
ACM SIGGRAPH 2007 papers
A Closed-Form Solution to Natural Image Matting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video SnapCut: robust video object cutout using localized classifiers
ACM SIGGRAPH 2009 papers
A duality based approach for realtime TV-L1 optical flow
Proceedings of the 29th DAGM conference on Pattern recognition
Dynamic color flow: a motion-adaptive color model for object segmentation in video
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Robust Stereo Matching Using Adaptive Normalized Cross-Correlation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time video matting based on bilayer segmentation
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
BRDF Invariant Stereo Using Light Transport Constancy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Occlusion-Aware Optical Flow Estimation
IEEE Transactions on Image Processing
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Given a segmentation result (an alpha matte or a binary mask) of the former frame, foreground prediction is a process of estimating the probability that each pixel in the current frame belongs to the foreground. It plays a very important role in bilayer segmentation of videos, especially videos with non-static backgrounds. In this paper, a new foreground prediction algorithm which is called opacity propagation is proposed. It can propagate the opacity values of the former frame to the current frame by minimizing a cost function that is constructed by assuming the spatiotemporally local color smoothness of the video. Optical flow and probability density estimation based on a local color model are employed to find the corresponding pixels of two adjacent frames. An OPSIC (opacity propagation with sudden illumination changes) algorithm is also proposed which is an improvement of our proposed opacity propagation algorithm because it adds a simple color transformation model. As far as we know, this is the first algorithm that can predict the foreground accurately when the illumination changes suddenly. The opacity map (OM) generated by the opacity propagation algorithm is usually more accurate than the previously used probability map (PM). The experiments demonstrate the effectiveness of our algorithm.