ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Efficient object-based video inpainting
Pattern Recognition Letters
Evolving virtual contents with interactions in videos
IMCE '09 Proceedings of the 1st international workshop on Interactive multimedia for consumer electronics
Exposing digital video forgery by ghost shadow artifact
MiFor '09 Proceedings of the First ACM workshop on Multimedia in forensics
Video completion via motion guided spatial-temporal global optimization
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Patch-based video processing: a variational Bayesian approach
IEEE Transactions on Circuits and Systems for Video Technology
Exemplar-based video inpainting without ghost shadow artifacts by maintaining temporal continuity
IEEE Transactions on Circuits and Systems for Video Technology
Video Inpainting on Digitized Old Films
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
Spatio-temporally Consistent Multi-view Video Synthesis for Autostereoscopic Displays
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Video object inpainting using posture mapping
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Video narrative authoring with motion inpainting
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
Video data hiding for managing privacy information in surveillance systems
EURASIP Journal on Information Security - Special issue on enhancing privacy protection in multimedia systems
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
How Not to Be Seen — Object Removal from Videos of Crowded Scenes
Computer Graphics Forum
Background inpainting for videos with dynamic objects and a free-moving camera
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
An efficient framework for image/video inpainting
Image Communication
A new approach of image inpainting in the wireless environment
International Journal of Ad Hoc and Ubiquitous Computing
de-linkability: a privacy-preserving constraint for safely outsourcing multimedia documents
Proceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems
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A framework for inpainting missing parts of a video sequence recorded with a moving or stationary camera is presented in this work. The region to be inpainted is general: It may be still or moving, in the background or in the foreground, it may occlude one object and be occluded by some other object. The algorithm consists of a simple preprocessing stage and two steps of video inpainting. In the preprocessing stage, we roughly segment each frame into foreground and background. We use this segmentation to build three image mosaics that help to produce time consistent results and also improve the performance of the algorithm by reducing the search space. In the first video inpainting step, we reconstruct moving objects in the foreground that are "occluded" by the region to be inpainted. To this end, we fill the gap as much as possible by copying information from the moving foreground in other frames, using a priority-based scheme. In the second step, we inpaint the remaining hole with the background. To accomplish this, we first align the frames and directly copy when possible. The remaining pixels are filled in by extending spatial texture synthesis techniques to the spatiotemporal domain. The proposed framework has several advantages over state-of-the-art algorithms that deal with similar types of data and constraints. It permits some camera motion, is simple to implement, fast, does not require statistical models of background nor foreground, works well in the presence of rich and cluttered backgrounds, and the results show that there is no visible blurring or motion artifacts. A number of real examples taken with a consumer hand-held camera are shown supporting these findings