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A new approach to robust fundamental matrix estimation using an analytic objective function and adjusted gradient projection
Author(s): Cuibing Du; Zongqing Lu; Jing-Hao Xue; Qingmin Liao
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Paper 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.

Paper Details

Date Published: 14 August 2019
PDF: 9 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111793D (14 August 2019); doi: 10.1117/12.2539648
Show Author Affiliations
Cuibing Du, Tsinghua Univ. (China)
Zongqing Lu, Tsinghua Univ. (China)
Jing-Hao Xue, Univ. College London (United Kingdom)
Qingmin Liao, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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