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Homography Estimation Based on Order-Preserving Constraint and Similarity Measurement
Zhu, Haijiang Wen, Xin Zhang, Fan Wang, Xuejing Wang, Guanghui
Zhu, Haijiang
Wen, Xin
Zhang, Fan
Wang, Xuejing
Wang, Guanghui
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Abstract
Homography is an important concept that has been extensively applied in many computer vision
applications. However, accurate estimation of the homography is still a challenging problem. The classical
approaches for robust estimation of the homography are all based on the iterative RANSAC framework.
In this paper, we explore the problem from a new perspective by nding four point correspondences between
two images given a set of point correspondences. The approach is achieved by means of an order-preserving
constraint and a similarity measurement of the quadrilateral formed by the four points. The proposed method
is computationally ef cient as it requires much less iterations than the RANSAC algorithm. But this method
is designed for small camera motions between consecutive frames in video sequences. Extensive evaluations
on both synthetic data and real images have been performed to validate the effectiveness and accuracy of the
proposed approach. In the synthetic experiments, we investigated and compared the accuracy of three types
of methods and the in uence of the proportion of outliers and the level of noise for homography estimation.
We also analyzed the computational cost of the proposed method and compared our method with the stateof-
the-art approaches in real image experiments. The experimental results show that the proposed method is
more robust than the RANSAC algorithm.
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Date
2018-05-17
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IEEE
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Keywords
Homography estimation, order-preserving constraint, Similarity measurement
Citation
H. Zhu, X. Wen, F. Zhang, X. Wang and G. Wang, "Homography Estimation Based on Order-Preserving Constraint and Similarity Measurement," in IEEE Access, vol. 6, pp. 28680-28690, 2018.
doi: 10.1109/ACCESS.2018.2837639