Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Motion Layer Based Object Removal in Videos
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Image completion with structure propagation
ACM SIGGRAPH 2005 Papers
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
Video Completion by Motion Field Transfer
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Image Completion Using Global Optimization
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Video Completion for Perspective Camera Under Constrained Motion
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Image inpainting by global structure and texture propagation
Proceedings of the 15th international conference on Multimedia
Region filling and object removal by exemplar-based image inpainting
IEEE Transactions on Image Processing
Video Inpainting Under Constrained Camera Motion
IEEE Transactions on Image Processing
Fast image rearrangement via multi-scale patch copying
Proceedings of the international conference on Multimedia
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Motion characteristic differentiated error concealment
Multimedia Tools and Applications
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In this paper, a novel global optimization based approach is proposed for video completion whose target is to restore the spatial-temporal missing regions of a video in a visually plausible way. Our algorithm consists of two stages: motion field completion and color completion via global optimization. First, local motions within the missing parts are completed patch-by-patch greedily using pre-computed available motions in the video. Then the missing regions are filled by sampling patches from available parts of the video. We formulate the video completion as a global energy minimization problem by Markov random fields (MRFs). Based on the completed motion field of the video, a well-defined energy function involving both spatial and temporal coherence relationship is constructed. A coarse-to-fine Belief Propagation (BP) is proposed to solve the optimization problem. Experimental results have demonstrated the good performance of our algorithm.