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Seamless video composition using optimized mean-value cloning
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AI
DOI：10.1007/s00371-010-0466-6.png)
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摘要
En 中文
As the process of pasting a source video patch into a target video sequence, seamless video composition is an important and useful video editing operation. Recently, a novel composition approach based on Mean-Value Coordinates has been presented. However, its composition result is often degraded by smudging and discoloration artifacts. Thus we propose optimized mean-value cloning to eliminate these artifacts by matting technique and interpolation constraint. On the basis of this optimized approach, we further present a new framework for seamless video composition. In the framework, we first develop a propagation model based on contour flow to yield each trimap that provides each frame with a pre-segmentation: definite foreground, definite background and unknown. This propagation model constructs the contour flow of inter-frame by minimizing a cost function, and employs it to relabel the trimap. Moreover, when the trimap propagation model is inefficient due to abrupt feature change and complex scene pattern, our framework has also implemented a convenient interactive tool to create and modify trimap. Then, we can use the high-quality trimap to achieve the optimized mean-value cloning. Experimental results demonstrate the effectiveness of our seamless video composition framework.
Keyword:
Mean-value coordinates
Matting and composition
Video processing