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Mathematical Problems in Engineering
Volume 2014, Article ID 452803, 14 pages
Research Article

Feature Based Stereo Matching Using Two-Step Expansion

1School of Instrumentation Science & Opto-Electronics Engineering, Beihang University, Beijing 100191, China
2National Institute of Metrology, Beijing 100029, China

Received 2 January 2014; Revised 19 June 2014; Accepted 21 July 2014; Published 18 December 2014

Academic Editor: Yi Chen

Copyright © 2014 Liqiang Wang 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.


This paper proposes a novel method for stereo matching which is based on image features to produce a dense disparity map through two different expansion phases. It can find denser point correspondences than those of the existing seed-growing algorithms, and it has a good performance in short and wide baseline situations. This method supposes that all pixel coordinates in each image segment corresponding to a 3D surface separately satisfy projective geometry of 1D in horizontal axis. Firstly, a state-of-the-art method of feature matching is used to obtain sparse support points and an image segmentation-based prior is employed to assist the first region outspread. Secondly, the first-step expansion is to find more feature correspondences in the uniform region via initial support points, which is based on the invariant cross ratio in 1D projective transformation. In order to find enough point correspondences, we use a regular seed-growing algorithm as the second-step expansion and produce a quasi-dense disparity map. Finally, two different methods are used to obtain dense disparity map from quasi-dense pixel correspondences. Experimental results show the effectiveness of our method.