Recent interesting issues in video inpainting are defect removal and object removal. We take one more step to replace the removed objects in a video sequence by implanting objects from another video. Before implant, we improve an exemplar-based image inpainting algorithm by using a new patch matching strategy which incorporates edge properties. The data term used in a priority computation of candidate patches is also redefined. We take varieties of temporal continuations of foreground and background into consideration. A motion compensated inpainting procedure is then proposed. The inpainted video backgrounds are visually pleasant with smooth transitions. A simple tracking algorithm is then used to produce a foreground video, which is implanted into the inpainted background video. Our results are available at http://www.mine.tku.edu.tw/inpainting.
In this paper, an adaptive approach to skeleton detection is presented. First, we proposed a method to combine thinning and tracking algorithm which can find the skeleton tracking points, and the points would be reconstructed by using multiple cameras to obtain the 3D coordinates the X, Y, and Z-axis coordinates. The coordinates can be converted into three-dimensional human avatar. The joints of human avatar can be found by the thinning algorithm, and using object tracking to find the joints when the objects are disappeared. The avatar can be applied to the application such as Human computer interaction, entertainments, etc.
This video program presents several video inpainting/implant mechanisms implemented under different types of videos with diversified temporal continuations. We use an improved image inpainting method to remove foreground objects. Another set of objects are implanted into the inpainted video. Detailed algorithms are presented in a paper in the 2006 ACM Multimedia conference.
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