Du, C;
Lu, Z;
Xue, JH;
Liao, Q;
(2019)
A new approach to robust fundamental matrix estimation using an analytic objective function and adjusted gradient projection.
In:
Proceedings of the Eleventh International Conference on Digital Image Processing (ICDIP 2019).
SPIE
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Abstract
In this paper we propose a new approach to tackling the challenging problem of robust fundamental matrix estimation from corrupted correspondences. Compared with traditional robust methods, the proposed approach achieves enhanced estimation accuracy and stability. These achievements are attributed mainly to two novelties contributed by the new approach. Firstly, a new, more easily-solvable analytic objective function is proposed to well consider both the presence of correspondence outliers and the computational convenience. Secondly, an adjusted gradient projection method is developed to provide a more stable solver for robust estimation. Experimental results show that the proposed approach performs better than traditional robust methods RANSAC, MSAC, LMEDS and MLESAC, in particular when correspondences were seriously corrupted.
| Type: | Proceedings paper |
|---|---|
| Title: | A new approach to robust fundamental matrix estimation using an analytic objective function and adjusted gradient projection |
| Event: | The Eleventh International Conference on Digital Image Processing (ICDIP 2019) |
| Location: | Guangzhou, China |
| Dates: | 10th-13th May 2019 |
| ISBN-13: | 9781510630758 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1117/12.2539648 |
| Publisher version: | https://doi.org/10.1117/12.2539648 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | fundamental matrix, analytic objective function, gradient projection, robust methods |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10083800 |
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