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Patchmatch-Based Robust Stereo Matching Under Radiometric Changes

机译:辐射变化下基于补丁匹配的鲁棒立体匹配

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摘要

In the real world, the two challenges of stereo vision system include a robust system under various radiometric changes and real-time process. To extract depth information from stereoscopic images, this paper proposes Patchmatch-based robust and fast stereo matching under radiometric changes. For this, a cost function was designed and minimized for estimating an accurate disparity map. Specifically, we used a prior probability to minimize the occlusion region and a smoothness term that considers convexity of objects to extract a fine disparity map. For evaluating the performance of the proposed scheme, we used Middlebury stereo data sets with radiometric changes. The experimental result showed that the proposed method outperforms state-of-the-art methods by up to 3.35 percent better and a range of 4.71 - 27.24 times faster result in terms of bad pixel error and processing time, respectively. Therefore, we believe that the proposed scheme can be a useful tool for computer vision-based applications.
机译:在现实世界中,立体视觉系统的两个挑战包括在各种辐射变化和实时过程下的强大系统。为了从立体图像中提取深度信息,本文提出了在辐射变化下基于Patchmatch的鲁棒和快速立体匹配。为此,设计了成本函数并将其最小化以估计准确的视差图。具体来说,我们使用先验概率来最小化遮挡区域,并使用考虑物体凸度的平滑度项来提取精细的视差图。为了评估所提出方案的性能,我们使用了米德伯里立体声数据集并进行了辐射测量。实验结果表明,在不良像素错误和处理时间方面,所提出的方法比最先进的方法好3.35%,并且结果快4.71-27.24倍。因此,我们相信所提出的方案可以成为基于计算机视觉的应用程序的有用工具。

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