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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 127284, 9 pages
Research Article

Efficient Stereo Matching with Decoupled Dissimilarity Measure Using Successive Weighted Summation

1School of Electronic Information Engineering, TianJin University, TianJin 300072, China
2Department of Mechanical Engineering, Chang Gung University, Taoyuan 33302, Taiwan
3Department of Neurosurgery, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan

Received 31 October 2013; Accepted 16 December 2013; Published 16 January 2014

Academic Editor: Yi-Hung Liu

Copyright © 2014 Cheng-Tao Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Developing matching algorithms from stereo image pairs to obtain correct disparity maps for 3D reconstruction has been the focus of intensive research. A constant computational complexity algorithm to calculate dissimilarity aggregation in assessing disparity based on separable successive weighted summation (SWS) among horizontal and vertical directions was proposed but still not satisfactory. This paper presents a novel method which enables decoupled dissimilarity measure in the aggregation, further improving the accuracy and robustness of stereo correspondence. The aggregated cost is also used to refine disparities based on a local curve-fitting procedure. According to our experimental results on Middlebury benchmark evaluation, the proposed approach has comparable performance when compared with the selected state-of-the-art algorithms and has the lowest mismatch rate. Besides, the refinement procedure is shown to be capable of preserving object boundaries and depth discontinuities while smoothing out disparity maps.