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A cross-scale constrained dynamic programming algorithm for stereo matching
Author(s): Sipei Cheng; Feipeng Da; Jian Yu; Yuan Huang; Shaoyan Gai
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Paper 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.

Paper Details

Date Published: 13 June 2017
PDF: 7 pages
Proc. SPIE 10449, Fifth International Conference on Optical and Photonics Engineering, 1044923 (13 June 2017); doi: 10.1117/12.2270830
Show Author Affiliations
Sipei Cheng, Southeast Univ. (China)
Feipeng Da, Southeast Univ. (China)
Jian Yu, Southeast Univ. (China)
Yuan Huang, Southeast Univ. (China)
Shaoyan Gai, Southeast Univ. (China)


Published in SPIE Proceedings Vol. 10449:
Fifth International Conference on Optical and Photonics Engineering
Anand Krishna Asundi, Editor(s)

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