| Summary: | Stereo matching is a difficult and challenging task due to many uncontrollable factors that affect the results. These factors include the non-ideal radiometric conditions and the presence of weak-textured regions. A stereo matching algorithm including cost computation, cost aggregation, disparity computation, and disparity refinement is proposed to overcome these limitations. First, census, gradient and HSL measures are combined as the pixel-wise matching cost to reduce the radiometric distortions. Second, a second-order smoothness constraint based on angle direction priors is utilized to improve the matching accuracy in the weak-textured regions. Then, the matching cost and smoothness constraint are applied to semi-global matching. Third, a winner-takes-all strategy is adopted to calculate the initial disparity maps. Finally, a coarse-to-fine simple linear iterative clustering superpixel segmentation is proposed to split the image into some regions with similar colors, adjacent pixels, and similar disparity planes. Through the segmentation result, boundary lines are classified into occluded, hinge, and coplanar lines. Slanted plane iterative optimization is performed to obtain the optimized disparity maps according to the segmentation result and boundary lines classification. The algorithm is evaluated in the Middlebury and KITTI datasets. The experimental results show that the method has good performance under non-ideal radiometric conditions and in the weak-textured regions. The disparity maps obtained by the method have a lower mismatching rate compared with other algorithms.
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