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

Cuibing Du; Zongqing Lu; Jing-Hao Xue; Qingmin Liao

doi:10.1117/12.2539648

14 August 2019 A new approach to robust fundamental matrix estimation using an analytic objective function and adjusted gradient projection

Cuibing Du, Zongqing Lu, Jing-Hao Xue, Qingmin Liao

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Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111793D (2019) https://doi.org/10.1117/12.2539648
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

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.

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Cuibing Du, Zongqing Lu, Jing-Hao Xue, and Qingmin Liao "A new approach to robust fundamental matrix estimation using an analytic objective function and adjusted gradient projection", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111793D (14 August 2019); https://doi.org/10.1117/12.2539648

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KEYWORDS

Cameras

Statistical analysis

Iterative methods

Error analysis

Optimization (mathematics)

Computing systems

Electronics engineering

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