Bi-layer segmentation of binocular stereo video
Bi-layer segmentation of binocular stereo video
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DOI：
10.1109/cvpr.2005.90
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发表时间：
2005-06
期刊：
影响因子：
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通讯作者：
V. Kolmogorov;A. Criminisi;A. Blake;G. Cross;C. Rother
中科院分区：
文献类型：
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作者：
V. Kolmogorov;A. Criminisi;A. Blake;G. Cross;C. Rother
本文描述了两种能够从立体视频序列的背景层中实时分割前景层的算法。从颜色/对比度或仅从立体声中自动分离图层是容易出错的。在这里，颜色，对比度和立体匹配信息融合，以准确有效地推断层。第一种算法，分层动态规划（LDP），在扩展的6状态空间中解决立体问题，该空间表示前景/背景层和遮挡区域。然后将立体匹配可能性与动态学习的对比敏感颜色模型融合，并通过动态规划获得立体差异。第二种算法是分层图切（LGC），它不直接解决立体问题。相反，立体匹配的可能性在前景和背景假设中被边缘化，并与对比度敏感的颜色模型融合，就像在LDP中使用的那样。采用三元图割法有效地解决了分割问题。这两种算法都是根据真实数据进行评估的，发现它们具有相似的p性能，比单独的立体或色彩/对比度要好得多。然而，它们在计算效率方面的特征是相当不同的。该算法在背景替换的应用中得到了验证，得到了高质量的合成视频输出。
This paper describes two algorithms capable of real-time segmentation of foreground from background layers in stereo video sequences. Automatic separation of layers from colour/contrast or from stereo alone is known to be error-prone. Here, colour, contrast and stereo matching information are fused to infer layers accurately and efficiently. The first algorithm, layered dynamic programming (LDP), solves stereo in an extended 6-state space that represents both foreground/background layers and occluded regions. The stereo-match likelihood is then fused with a contrast-sensitive colour model that is learned on the fly, and stereo disparities are obtained by dynamic programming. The second algorithm, layered graph cut (LGC), does not directly solve stereo. Instead the stereo match likelihood is marginalised over foreground and background hypotheses, and fused with a contrast-sensitive colour model like the one used in LDP. Segmentation is solved efficiently by ternary graph cut. Both algorithms are evaluated with respect to ground truth data and found to have similar p performance, substantially better than stereo or colour/contrast alone. However, their characteristics with respect to computational efficiency are rather different. The algorithms are demonstrated in the application of background substitution and shown to give good quality composite video output.