Abstract
Adaptive cross-region-based guided image filtering (ACR-GIF) is a commonly used cost aggregation method. However, the weights of points in the adaptive cross-region (ACR) are generally not considered, which affects the accuracy of disparity results. In this study, we propose an improved cost aggregation method to address this issue. First, the orthogonal weight is proposed according to the structural feature of the ACR, and then the orthogonal weight of each point in the ACR is computed. Second, the matching cost volume is filtered using ACR-GIF with orthogonal weights (ACR-GIF-OW). In order to reduce the computing time of the proposed method, an efficient weighted aggregation computing method based on orthogonal weights is proposed. Additionally, by combining ACR-GIF-OW with our recently proposed matching cost computation method and disparity refinement method, a local stereo matching algorithm is proposed as well. The results of Middlebury evaluation platform show that, compared with ACR-GIF, the proposed cost aggregation method can significantly improve the disparity accuracy with less additional time overhead, and the performance of the proposed stereo matching algorithm outperforms other state-of-the-art local and nonlocal algorithms.
Highlights
Binocular stereo vision can acquire disparity information with required accuracy only by using image pairs of the same scene that are obtained from different angles
In error maps of Adaptive cross-region-based guided image filtering (ACR-guided image filtering (GIF))-OW, black regions in red boxes are apparently smaller in area, and these red boxes mainly correspond to weakly textured and textureless regions in the image. e above result indicates that adaptive cross-region (ACR)-GIF-OW can improve the disparity accuracy of these regions, thereby improving the overall disparity accuracy
Combining the results acquired in Sections 4.2.1 and 4.2.2, we can conclude that comparing the increase in time overhead shows that ACR-GIF-OW exhibits an obvious improvement with regard to the disparity accuracy. us, considering both the accuracy and the time overhead, the proposed method is advantageous over ACR-GIF
Summary
Binocular stereo vision can acquire disparity information with required accuracy only by using image pairs of the same scene that are obtained from different angles. Local algorithms are based on cost aggregation within the specified support region, and the matching accuracy is usually lower than those of the first two types. Cost aggregation refers to summing or averaging the matching cost in the support region of each pixel, which directly influences the computing efficiency and accuracy of the algorithm. It is one of the most important steps and the primary focus of many studies. Adaptive cross-region-based guided image filtering (ACR-GIF) is a cost aggregation method adopted by many local stereo matching algorithms currently [22,23,24,25,26]. (3) Combining ACR-GIF-OW with our recently proposed matching cost computation and disparity refinement methods, a local stereo matching algorithm is proposed.
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