UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

A new approach to robust fundamental matrix estimation using an analytic objective function and adjusted gradient projection

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 Green open access

[img]
Preview
Text
Xue_A new approach to robust fundamental matrix estimation using an analytic objective function and adjusted gradient projection_VoR.pdf - Published version

Download (1MB) | Preview

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.ucl.ac.uk/id/eprint/10083800
Downloads since deposit
4Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item