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Obtaining depth map from segment-based stereo matching using graph cuts

机译：使用图割从基于片段的立体匹配中获取深度图

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摘要

In the paper, the algorithm of segment-based stereo matching using graph cuts is developed for extracting depth information from the stereo image pairs. The first step of the algorithm employs the mean-shift algorithm to segment the reference image, which ensures our method to correctly estimate in large untextured regions and precisely localize depth boundaries, followed by the use of Adaptive Support Weighted Self-Adaptation dissimilarity algorithm (ASW-SelfAd) for the estimation of initial disparity. This is followed by application of Singular Value Decomposition (SVD) in solving the robust disparity plane fitting. In order to ensure reliable pixel sets for the segment, we filter out outliers which contain occlusion region through three main rules, namely; cross-checking, judging reliable area and disparity distance measurement. Lastly, we apply improved clustering algorithm to merge the neighboring segments. The geometrical relationship of adjacent planes such as parallelism and intersection is employed for determination of whether two planes shall be merged. A new energy function is subsequently formulated with the use of graph cuts for the refinement of the disparity map. Finally, the depth information is extracted from the final disparity map. Experimental results on the Middlebury dataset demonstrate that our approach is effective in improving the state of the art.
机译：本文提出了一种基于图割的基于片段的立体匹配算法，用于从立体图像对中提取深度信息。该算法的第一步采用均值漂移算法对参考图像进行分割，这确保了我们的方法能够在较大的无纹理区域中正确估计并精确定位深度边界，然后使用自适应支持加权自适应非相似性算法（ASW） -SelfAd）来估计初始差异。其次是在解决鲁棒的视差平面拟合中应用奇异值分解（SVD）。为了确保该段的像素集可靠，我们通过三个主要规则过滤出包含遮挡区域的离群值：交叉检查，判断可靠的区域和视差距离测量。最后，我们应用改进的聚类算法来合并相邻的段。相邻平面的几何关系（如平行度和相交）用于确定是否应合并两个平面。随后使用图割来制定新的能量函数，以完善视差图。最后，从最终视差图中提取深度信息。在Middlebury数据集上的实验结果表明，我们的方法可以有效地改善现有技术。

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