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Mathematical Problems in Engineering
Volume 2013, Article ID 654139, 11 pages
Research Article

Matching Cost Filtering for Dense Stereo Correspondence

1Institute of Optical Communication & Optoelectronics, Beijing University of Posts & Telecommunications, Beijing 100876, China
2School of Instrumentation Science & Optoelectronics Engineering, Beijing Information Science & Technology University, Beijing 100192, China
3Beijing Aeronautical Manufacturing Technology Research Institute, Beijing 100024, China

Received 4 July 2013; Accepted 27 August 2013

Academic Editor: Vishal Bhatnaga

Copyright © 2013 Yimin Lin 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.


Dense stereo correspondence enabling reconstruction of depth information in a scene is of great importance in the field of computer vision. Recently, some local solutions based on matching cost filtering with an edge-preserving filter have been proved to be capable of achieving more accuracy than global approaches. Unfortunately, the computational complexity of these algorithms is quadratically related to the window size used to aggregate the matching costs. The recent trend has been to pursue higher accuracy with greater efficiency in execution. Therefore, this paper proposes a new cost-aggregation module to compute the matching responses for all the image pixels at a set of sampling points generated by a hierarchical clustering algorithm. The complexity of this implementation is linear both in the number of image pixels and the number of clusters. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art local methods in terms of both accuracy and speed. Moreover, performance tests indicate that parameters such as the height of the hierarchical binary tree and the spatial and range standard deviations have a significant influence on time consumption and the accuracy of disparity maps.