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基于模糊连接度的抠图样本集构造方法 被引量：5

Color Sampling Based on Fuzzy Connectedness for Image Matting
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摘要 针对已有的图像抠图采样方法易受trimap输入的影响且精确度不足的问题,提出一种基于模糊连接度的抠图样本集构造方法.通过计算模糊连接度求解未知像素到前景边界和背景边界的最强路径,以与最强路径关联的已知像素为中心搜集邻近的已知像素,并构造出未知像素的样本集,且当新的用户笔画加入后,能够快速地更新样本.实验结果表明,文中方法对trimap的依赖性小、采样精确度高、鲁棒性强. In this paper a color sampling approach based on fuzzy eonnectedness for image matting is presented. Firstly, the strongest paths between unknown pixels and boundary of foreground （or background） are calculated using fuzzy connectedness. Secondly, the known pixels associated to the strongest paths are found. Finally, centered on the associated known pixels, adjacent known pixels are collected to construct sample sets of the unknown pixels. Furthermore, the method can rapidly update samples when new strokes drawn by user are added. Comparative experiments on a variety of input images with different trimaps are presented which demonstrate the accuracy and robustness of the proposed method.
作者 吴玉娥 何发智 蔡贤涛 黄志勇 李小霞
机构地区 武汉大学计算机科学与技术学院
出处 《计算机辅助设计与图形学学报》 EI CSCD 北大核心 2010年第7期1194-1200,共7页 Journal of Computer-Aided Design & Computer Graphics
基金 国家自然科学基金(60673027) 国家自然科学基金国际合作项目(60811140344) 中央高校基本科研业务费专项资金 武汉大学博士研究生自主科研基金
关键词 图像抠图 颜色采样 模糊连接度 最强路径 image matting color sampling fuzzy connectedness the strongest path
分类号 TP391 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献9

  • 1Chuang Y Y, Brian C, David H S, etal. A Bayesian approach to digital matting [C] //Proceedings of IEEE International Confcrcncc on Computer Vision and Pattern Recognition, Hawaii, 2001, 2:264-271.
  • 2林生佑,石教英.基于感知颜色空间的自然图像抠图[J].计算机辅助设计与图形学学报,2005,17(5):915-920. 被引量：16
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二级参考文献8

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共引文献15

同被引文献45

  • 1林生佑,石教英.基于感知颜色空间的自然图像抠图[J].计算机辅助设计与图形学学报,2005,17(5):915-920. 被引量：16
  • 2Boykov Y, Jolly M P. Interactive graph cuts for optimal boundary& region segmentation of objects in N-D images [C] //Proceedings of International Conference on Computer Vision. Los Alamitos: IEEE Computer Society Press, 2001, 1:105-112.
  • 3Boykov Y, Funka-Lea G. Graph cuts and efficient N-D image segmentation [J].International Journal of Computer Vision, 2006, 70(2): 109-131.
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  • 5Bai X, Sapiro G. A geodesic framework for fast interactive image and video segmentation and matting [C] //Proceedings of International Conference on Computer Vision. Los Alamitos: IEEE Computer Society Press, 2007:1-8.
  • 6Price B L, Morse B, Cohen S. Geodesic graph cut for interaetive image segmentation [C]//Proeeedings of Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, 2010:3161-3168.
  • 7Xiang S M, Nie F D, Zhang C X, et al. Interactive natural image segmentation via spline regression [J]. IEEE Transactions on Image Processing, 2009, 18(7) : 1623-1632.
  • 8Xiang S M, -Nie F P, Zhang C S. Semi-supervised classification via local spline regression[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(11): 2039-2053.
  • 9Rother C, Kolmogorov V, Blake A. Grabcut: Interactive foreground extraction using iterated graph cuts [C] // Computer Graphics Proceedings, Annual Conference Series, ACM SIGGRAPH. New York: ACM Press, 2004: 309-314.
  • 10Li Y, Sun J, Tang C K, etal. Lazy snapping [C]//Computer Graphics Proceedings, Annual Conference Series, ACM SIGGRAPH. New York: ACM Press, 2004:303-308.

引证文献5

二级引证文献30

计算机辅助设计与图形学学报
2010年 第7期

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