Texture discrimination-enforced matching cost computation and smoothness-weighted cost regularization for stereo matching
Texture discrimination-enforced matching cost computation and smoothness-weighted cost regularization for stereo matching
复制标题
用于立体匹配的纹理辨别强制匹配成本计算和平滑度加权成本正则化
DOI:
10.1117/1.jei.28.5.053025
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发表时间:
2019-10
影响因子:
1.1
通讯作者:
Luo Ying
中科院分区:
文献类型:
--
作者:
Xu Jinxin;Li Qingwu;Zhou Yan;Liu Yan;Luo Ying
Abstract. In stereo matching, energy-minimization optimizers such as dynamic programming (DP) could achieve efficient disparity estimation. However, DP-based optimizers still suffer from relatively high energy due to too many artificial penalty parameters and a complex energy function, inevitably leading to matching failure in depth discontinuities, occlusions, and low-textured regions. To reach dense high-accuracy disparity estimation, we propose an effective stereo-matching method using texture discrimination-enforced matching cost computation and smoothness-weighted cost regularization. First, pixel adjustment is introduced to the gradient difference calculation so that initial matching accuracy, especially in low-textured regions, can be improved. For determining the degree of pixel adjustment, a gray-gradient co-occurrence matrix and the fuzzy c-means method are adopted to differentiate between two varieties of reference images based on textural property. Second, the support weight in local methods and the smoothness constraints in global optimizations are leveraged. Color information and the confidence map of the input image are combined to form the support weight. By ameliorating the smoothness constraint function during weighted horizontal DP passes, the adverse effects of excessive penalty can be reduced. Third, a postprocessing method is proposed for refining results in both occluded and low-textured regions. The proposed method considers test cases from the Middlebury v.2 and v.3 datasets and can outperform the state-of-the-art stereo-matching algorithms.
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