Exemplar-based background model initialization
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
Full-Frame Video Stabilization with Motion Inpainting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Space-Time Completion of Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Efficient object-based video inpainting
Pattern Recognition Letters
Patch-based video processing: a variational Bayesian approach
IEEE Transactions on Circuits and Systems for Video Technology
Improving Intra Coding in H.264\AVC by Image Epitome
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Video super-resolution using motion compensation and classification-aided fusion
Proceedings of the 24th Spring Conference on Computer Graphics
RBF based spatio-temporal representation technique for video compression
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Epitomized summarization of wireless capsule endoscopic videos for efficient visualization
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Inpainting with image patches for compression
Journal of Visual Communication and Image Representation
Generation of an omnidirectional video without invisible areas using image inpainting
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Using photographs to enhance videos of a static scene
EGSR'07 Proceedings of the 18th Eurographics conference on Rendering Techniques
Advanced Engineering Informatics
Summarizing high-level scene behavior
Machine Vision and Applications
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Recently, "epitomes" were introduced as patch-based probability models that are learned by compiling together a large number of examples of patches from input images. In this paper, we describe how epitomes can be used to model video data and we describe significant computational speedups that can be incorporated into the epitome inference and learning algorithm. In the case of videos, epitomes are estimated so as to model most of the small space-time cubes from the input data. Then, the epitome can be used for various modeling and reconstruction tasks, of which we show results for video super-resolution, video interpolation, and object removal. Besides computational efficiency, an interesting advantage of the epitome as a representation is that it can be reliably estimated even from videos with large amounts of missing data. We illustrate this ability on the task of reconstructing the dropped frames in video broadcast using only the degraded video.