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Proceedings Paper

Binary adaptive semi-global matching based on image edges
Author(s): Han Hu; Yuri Rzhanov; Philip J. Hatcher; R. Daniel Bergeron
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Paper Abstract

Image-based modeling and rendering is currently one of the most challenging topics in Computer Vision and Photogrammetry. The key issue here is building a set of dense correspondence points between two images, namely dense matching or stereo matching. Among all dense matching algorithms, Semi-Global Matching (SGM) is arguably one of the most promising algorithms for real-time stereo vision. Compared with global matching algorithms, SGM aggregates matching cost from several (eight or sixteen) directions rather than only the epipolar line using Dynamic Programming (DP). Thus, SGM eliminates the classical “streaking problem” and greatly improves its accuracy and efficiency. In this paper, we aim at further improvement of SGM accuracy without increasing the computational cost. We propose setting the penalty parameters adaptively according to image edges extracted by edge detectors. We have carried out experiments on the standard Middlebury stereo dataset and evaluated the performance of our modified method with the ground truth. The results have shown a noticeable accuracy improvement compared with the results using fixed penalty parameters while the runtime computational cost was not increased.

Paper Details

Date Published: 6 July 2015
PDF: 7 pages
Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 96311D (6 July 2015); doi: 10.1117/12.2196960
Show Author Affiliations
Han Hu, Univ. of New Hampshire (United States)
Yuri Rzhanov, Univ. of New Hampshire (United States)
Philip J. Hatcher, Univ. of New Hampshire (United States)
R. Daniel Bergeron, Univ. of New Hampshire (United States)


Published in SPIE Proceedings Vol. 9631:
Seventh International Conference on Digital Image Processing (ICDIP 2015)
Charles M. Falco; Xudong Jiang, Editor(s)

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