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从视频到Flash矢量动画的自动转换 被引量：2

Automatic Conversion from Video Clip to Flash Animation
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摘要 提出一种自动将视频转换成矢量动画的算法,首先修改密度函数的计算方法,改进了均值漂移算法,对整个视频进行颜色分割,得到三维视频分割结果;然后通过边界追踪算法,用Bézier曲线表示边界,完成色块边界平滑和矢量化.设计人员可以在此基础上进行修改,使得非专业人员也能制作出复杂的Flash动画. This paper describes an automatic framework to convert a video clip to a vectorized animation. First the whole video is segmented by an improved Anisotropic Kernel Mean Shift algorithm where the computation of density function has been refined, and 3D segmentations of the video are obtained. Then by contouring tracking, and representation by Bezier curves, the color patches are retrieved. Finally the color patches are smoothed between frames to produce a continuous vectorized animation. By a little editing on this conversion result, non-professional designers can produce complex and nice Flash animations.
作者 张松海 陈韬 胡事民
机构地区 清华大学计算机科学与技术系
出处 《计算机辅助设计与图形学学报》 EI CSCD 北大核心 2007年第5期667-671,共5页 Journal of Computer-Aided Design & Computer Graphics
基金 国家"九七三"重点基础研究发展规划项目(2006CB303106) 教育部博士点基金(20060003057)
关键词 均值漂移 视频矢量化 密度函数 BÉZIER曲线 anisotropic kernel mean shift video vectorization density function fitting Bezier curves
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献11

  • 1Agarwala Aseem.Snaketoonz:a semi-automatic approach to creating cel animation from video[C]//Proceedings of the 2nd International Symposium on Non-Photorealistic Animation and Rendering.Annecy:ACM Press,2002:139-ff
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同被引文献13

  • 1龙晓苑,彭云.自动绘画笔触的设计与实现[J].计算机辅助设计与图形学学报,2005,17(3):623-626. 被引量：4
  • 2Cheng Y Z. Mean Shift, mode seeking, and clustering [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(8) : 790-799
  • 3Zhi Qiangwei, Cai Zixing. Mean shift algorithm and its application in tracking of objects [C]//Proceedings of the 5th International Conference on Machine Learning and Cybernetics, Dalian, 2006:13-16
  • 4Jeong MunHo, You Bum-Jae, Oh Yonghwan, et al. Adaptive mean-shift tracking with novel color model[ C] //Proceedings of the IEEE International Conference on Mechatronics & Automation Niagara Falls, Ontaric, 2005; 1329-1333
  • 5Yang Changjiang, Duraiswami Ramani, Davis Larry. Efficient mean-shift tracking via a new similarity measure [C] //Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition ( CVPR' 05 ), San Diego, 2005:176-183
  • 6Luo Qiming, Khoshgoftaar Taghi M. Efficient image segmentation by mean shift clustering and MDL-guided region merging [C] //Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004), Boca Raton, 2004:337-343
  • 7Pan Chen, Zheng Congxun, Wang Haojun. Robust color image segmentation based on. mean shift and marker-controlled watershed algorithm [C] //Proceedings of the 2nd International Conference on Machine Learning and Cybernetics, Xi' an, 2003 : 2752-2756
  • 8Cheng Yizong. Mean shift, mode seeking, and clustering [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(8): 790-799
  • 9Subbarao Raghav, Meer Peter. Nonlinear mean shift for clustering over analytic manifolds [C]//Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR' 06), New York, 2006 : 1168-1175
  • 10Li Yingqi, He Mingyi. Texture-based segmentation of high resolution SAR imagos using contourlet transforn'l and mean shift [C] //Proceedings of the 2006 IEEE International Conference on Information Acquisition, Weihai, 2006:201-206

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计算机辅助设计与图形学学报
2007年 第5期

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