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基于RGB向量空间中自适应支持权重方法的立体匹配

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

作者信息

Geng Yingnan, Zhao Yan, Chen Hexin

机构信息

College of Communication Engineering, Jilin University, Changchun 130012, China.

出版信息

Appl Opt. 2012 Jun 1;51(16):3538-45. doi: 10.1364/AO.51.003538.

Abstract

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.

摘要

梯度相似性是一种简单却强大的数据描述符,在立体匹配中表现出稳健性。本文为立体匹配定义了一个RGB向量空间。基于自适应支持权重方法,提出了一种匹配算法,该算法利用像素梯度相似性、颜色相似性以及RGB向量空间中的接近度来计算相应的支持权重和不相似性度量。在Middlebury立体基准测试中对实验结果进行了评估,结果表明我们的算法优于其他立体匹配算法,并且具有梯度相似性的算法在立体匹配中能取得更好的结果。

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