A fast matching algorithm with an adaptive window based on quasi-dense method

This paper presents a fast quasi-dense matching algorithm using an adaptive window. The algorithm starts from a set of sparse seed matches, and then propagates to the neighboring pixels; finally the most points in the images are matched. During computing the normalized cross correlation (NCC), an ad...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 3; pp. 1641 - 1646
Main Authors Guo-Zun Men, Jia-Li Chai, Jie Zhao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Adaptive Window and Quasi-Dense Matching
Application software
Computer vision
Confidence Coefficient
Cybernetics
Educational institutions
Incremental Computation
Machine learning
Machine learning algorithms
Normalized Cross Correlation
Optimization methods
Pixel
Stereo vision
Testing
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Summary:This paper presents a fast quasi-dense matching algorithm using an adaptive window. The algorithm starts from a set of sparse seed matches, and then propagates to the neighboring pixels; finally the most points in the images are matched. During computing the normalized cross correlation (NCC), an additional level of incremental calculation is proposed to achieve further speed-up. The confidence coefficient is introduced and the search window is varied in inverse proportion to it. The algorithm has been tested with stereo images and the results demonstrate its accuracy and efficiency. The algorithm also can be applied to a wide range of image pairs including those with large disparity or without rectification even if part of the images is less textured. In particular, with big images and a large disparity range our algorithm turns out to be significantly faster.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212272