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Internal and external similarity aggregation stereo match algorithm
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Paper Abstract

When extracting disparity map, for the high non-occlusion region error rate and low efficiency of stereo matching algorithms, we defined internal similarity (IS) of neighbor pixels in reference color image, and in the color and sub-pixel space, we defined external similarity (ES) of candidate matching pixels between the reference and target images, and given the calculation methods of the internal similarity and external similarity. We discussed the relationship between the internal similarity and external similarity, and taken the internal similarity as the aggregation degree of the external similarity, and the internal and external similarity aggregation (IESA) algorithm was proposed to aggregate the external similarity by the internal similarity along eight directions, after that, the disparity map was calculated by the winner take all (WTA) algorithm, which was used to search the disparity corresponding to the maximum aggregation similarity. Finally, the box plot filter (BPF) was proposed to smooth the disparity map. Experiments on the Middlebury stereo datasets and the extend datasets show that the proposed algorithms achieve the state-of-the-art results.

Paper Details

Date Published: 14 August 2019
PDF: 9 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117923 (14 August 2019); doi: 10.1117/12.2539674
Show Author Affiliations
Zhan Zhang, Univ. of Chinese Academy of Sciences (China)
Shenyang Institute of Computing Technology (China)
Dongsheng Yang, Shenyang Institute of Computing Technology (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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