Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/104021
Title: Robust homography estimation based on nonlinear least squares optimization
Authors: Mou, Wei
Wang, Han
Seet, Gerald
Keywords: DRNTU::Science::Mathematics
Issue Date: 2014
Source: Mou, W., Wang, H., & Seet, G. (2014). Robust Homography Estimation Based on Nonlinear Least Squares Optimization. Mathematical Problems in Engineering, 2014, 897050-.
Series/Report no.: Mathematical problems in engineering
Abstract: 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.
URI: https://hdl.handle.net/10356/104021
http://hdl.handle.net/10220/19386
ISSN: 1024-123X
DOI: 10.1155/2014/897050
Rights: © 2014 The Author(s).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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles
MAE Journal Articles

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