13 June 2017 A cross-scale constrained dynamic programming algorithm for stereo matching
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Proceedings Volume 10449, Fifth International Conference on Optical and Photonics Engineering; 1044923 (2017) https://doi.org/10.1117/12.2270830
Event: Fifth International Conference on Optical and Photonics Engineering, 2017, Singapore, Singapore
Abstract
Stereo matching is an important and hot research topic in computer vision. In order to solve the well-known streaking effects of dynamic programming, and reduce the mismatch points on edges, discontinuous and textureless regions, we propose a cross-scale constrained dynamic programming algorithm for stereo matching. The algorithm involves both image pyramid model and Gaussian scale space to operate a coarse-to-fine dynamic programming on multi-scale cost volumes. For the purpose of improving the disparity accuracy in textureless region, a cross-scale regularized constraint is added to ensure the cost consistency, the computational burden is reduced by using the disparity estimation from lower scale operation to seed the search on the larger image. Both synthetic and real scene experimental results show our algorithm can effectively reduce the mismatch in textureless regions.
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Sipei Cheng, Feipeng Da, Jian Yu, Yuan Huang, Shaoyan Gai, "A cross-scale constrained dynamic programming algorithm for stereo matching ", Proc. SPIE 10449, Fifth International Conference on Optical and Photonics Engineering, 1044923 (13 June 2017); doi: 10.1117/12.2270830; https://doi.org/10.1117/12.2270830
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