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Stereo matching based on adaptive support-weight approach in RGB vector space

机译:RGB向量空间中基于自适应支持权方法的立体匹配

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摘要

Gradient similarity is a simple, yet powerful, data descriptor which shows robustness in stereo matching. In this paper, a RGB vector space is defined for stereo matching. Based on the adaptive support-weight approach, a matching algorithm, which uses the pixel gradient similarity, color similarity, and proximity in RGB vector space to compute the corresponding support-weights and dissimilarity measurements, is proposed. The experimental results are evaluated on the Middlebury stereo benchmark, showing that our algorithm outperforms other stereo matching algorithms and the algorithm with gradient similarity can achieve better results in stereo matching.
机译:梯度相似度是一个简单但功能强大的数据描述符,它在立体声匹配中显示出鲁棒性。在本文中,为立体匹配定义了RGB矢量空间。基于自适应支撑权重方法,提出了一种匹配算法,该算法利用像素梯度相似度,颜色相似度和RGB矢量空间中的邻近度来计算相应的支撑权重和相异性度量。在Middlebury立体声基准上评估了实验结果,表明我们的算法优于其他立体声匹配算法,并且具有梯度相似性的算法可以在立体声匹配中获得更好的结果。

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