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Mathematical Problems in Engineering
Volume 2014, Article ID 897050, 8 pages
Research Article

Robust Homography Estimation Based on Nonlinear Least Squares Optimization

1School of Electrical & Electronics Engineering, Nanyang Technological University, Singapore
2School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore

Received 24 October 2013; Accepted 14 January 2014; Published 26 February 2014

Academic Editor: Yi-Hung Liu

Copyright © 2014 Wei Mou 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.


The homography between image pairs is normally estimated by minimizing a suitable cost function given 2D keypoint correspondences. The correspondences are typically established using descriptor distance of keypoints. However, the correspondences are often incorrect due to ambiguous descriptors which can introduce errors into following homography computing step. There have been numerous attempts to filter out these erroneous correspondences, but it is unlikely to always achieve perfect matching. To deal with this problem, we propose a nonlinear least squares optimization approach to compute homography such that false matches have no or little effect on computed homography. Unlike normal homography computation algorithms, our method formulates not only the keypoints’ geometric relationship but also their descriptor similarity into cost function. Moreover, the cost function is parametrized in such a way that incorrect correspondences can be simultaneously identified while the homography is computed. Experiments show that the proposed approach can perform well even with the presence of a large number of outliers.