Interactive sketch animation by graph matching integrated with learning boundary detection
Liangmei Hu, Henan Qu, Han Lv · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
In this paper, we study an integrated framework to generate expressive sketch animation from real video with user interaction. It consists of two mainly steps: (i) image sketch computation by a learning-based edge detector; (ii) temporal sketch propagation by a robust stochastic matching algorithm. In the first step, given a video clip, the edge probability map on each frame is first computed by a discriminative model that is trained with a collection of various features. A template sketch is flexibly extracted from the beginning frame by threshold tuning, where user intervention is allowed to perfect the sketch template. Then this template is matched and localized to the following image sketches over frames by the graph-based matching algorithm. User interaction is allowed to sequentially correct the matching results. A number of sketch animations from real videos are presented to verify this framework in the experiments.