The area of computer vision is one of the most discussed topics amongst many scholars, and stereo matching is its most important sub fields. After the parallax map is transformed into a depth map, it can be applied to many intelligent fields. In this paper, a stereo matching algorithm based on visual sensitive information is proposed by using standard images from Middlebury dataset. Aiming at the limitation of traditional stereo matching algorithms regarding the cost window, a cost aggregation algorithm based on the dynamic window is proposed, and the disparity image is optimized by using left and right consistency detection to further reduce the error matching rate. The experimental results show that the proposed algorithm can effectively enhance the stereo matching effect of the image providing significant improvement in accuracy as compared with the classical census algorithm. The proposed model code, dataset, and experimental results are available at https://github.com/WangHewei16/Stereo-Matching.
翻译:计算机视觉领域是许多学者中讨论最多的话题之一,立体匹配是其最重要的子领域。 在将准星图转换成深度地图后,它可以应用于许多智能领域。 在本文中, 使用Middlebury数据集的标准图像, 提出了基于视觉敏感信息的立体匹配算法。 为了限制传统立体匹配算法与成本窗口的匹配, 提出了基于动态窗口的成本组合算法, 通过使用左侧和右侧的一致性探测来优化差异图像, 以进一步降低误差匹配率。 实验结果显示, 拟议的算法可以有效地提高图像的立体匹配效果, 与古典普查算法相比, 使准确性显著提高。 拟议的模式代码、 数据集和实验结果可在 https://gitub.com/ WangHewei16/Stereo-Matching 上查阅。