Homography Estimation Based on Order-Preserving Constraint and Similarity Measurement
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....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8360418/ |
| _version_ | 1818853902634713088 |
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| author | Haijiang Zhu Xin Wen Fan Zhang Xuejing Wang Guanghui Wang |
| author_facet | Haijiang Zhu Xin Wen Fan Zhang Xuejing Wang Guanghui Wang |
| author_sort | Haijiang Zhu |
| collection | DOAJ |
| description | 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 finding 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 efficient 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 influence 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 state-of-the-art approaches in real image experiments. The experimental results show that the proposed method is more robust than the RANSAC algorithm. |
| first_indexed | 2024-12-19T07:44:12Z |
| format | Article |
| id | doaj.art-dfa29fbfcbe2418c9021a40119a2b12a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:44:12Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-dfa29fbfcbe2418c9021a40119a2b12a2022-12-21T20:30:24ZengIEEEIEEE Access2169-35362018-01-016286802869010.1109/ACCESS.2018.28376398360418Homography Estimation Based on Order-Preserving Constraint and Similarity MeasurementHaijiang Zhu0https://orcid.org/0000-0002-0609-3610Xin Wen1Fan Zhang2https://orcid.org/0000-0002-2058-2373Xuejing Wang3Guanghui Wang4https://orcid.org/0000-0003-3182-104XCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing, ChinaCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing, ChinaCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing, ChinaCenter for Information Technology, Beijing University of Chemical Technology, Beijing, ChinaDepartment of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, USAHomography 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 finding 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 efficient 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 influence 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 state-of-the-art approaches in real image experiments. The experimental results show that the proposed method is more robust than the RANSAC algorithm.https://ieeexplore.ieee.org/document/8360418/Homography estimationorder-preserving constraintsimilarity measurement |
| spellingShingle | Haijiang Zhu Xin Wen Fan Zhang Xuejing Wang Guanghui Wang Homography Estimation Based on Order-Preserving Constraint and Similarity Measurement IEEE Access Homography estimation order-preserving constraint similarity measurement |
| title | Homography Estimation Based on Order-Preserving Constraint and Similarity Measurement |
| title_full | Homography Estimation Based on Order-Preserving Constraint and Similarity Measurement |
| title_fullStr | Homography Estimation Based on Order-Preserving Constraint and Similarity Measurement |
| title_full_unstemmed | Homography Estimation Based on Order-Preserving Constraint and Similarity Measurement |
| title_short | Homography Estimation Based on Order-Preserving Constraint and Similarity Measurement |
| title_sort | homography estimation based on order preserving constraint and similarity measurement |
| topic | Homography estimation order-preserving constraint similarity measurement |
| url | https://ieeexplore.ieee.org/document/8360418/ |
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