The estimation of the fundamental matrix from a set of corresponding points is a relevant topic in epipolar stereo geometry [10]. Due to the high amount of outliers between the matches, RANSAC-based approaches [7, 13, 29] have been used to obtain the fundamental matrix. In this paper two new contributes are presented: a new normalized epipolar error measure which takes into account the shape of the features used as matches [17] and a new strategy to compare fundamental matrices. The proposed error measure gives good results and it does not depend on the image scale. Moreover, the new evaluation strategy describes a valid tool to compare different RANSAC-based methods because it does not rely on the inlier ratio, which could not correspond to the best allowable fundamental matrix estimated model, but it makes use of a reference ground truth fundamental matrix obtained by a set of corresponding points given by the user

Bellavia, F., Tegolo, D. (2011). noRANSAC for fundamental matrix estimation. In J. Hoey, S. McKenna, E. Trucco (a cura di), Proceedings of 22th British Machine Vision Conference, BMVC 2011 (pp. 1-11). dundee : BMVA Press [10.5244/C.25.98].

noRANSAC for fundamental matrix estimation

Bellavia, F;TEGOLO, Domenico
2011-01-01

Abstract

The estimation of the fundamental matrix from a set of corresponding points is a relevant topic in epipolar stereo geometry [10]. Due to the high amount of outliers between the matches, RANSAC-based approaches [7, 13, 29] have been used to obtain the fundamental matrix. In this paper two new contributes are presented: a new normalized epipolar error measure which takes into account the shape of the features used as matches [17] and a new strategy to compare fundamental matrices. The proposed error measure gives good results and it does not depend on the image scale. Moreover, the new evaluation strategy describes a valid tool to compare different RANSAC-based methods because it does not rely on the inlier ratio, which could not correspond to the best allowable fundamental matrix estimated model, but it makes use of a reference ground truth fundamental matrix obtained by a set of corresponding points given by the user
2011
1-901725-43-X
Bellavia, F., Tegolo, D. (2011). noRANSAC for fundamental matrix estimation. In J. Hoey, S. McKenna, E. Trucco (a cura di), Proceedings of 22th British Machine Vision Conference, BMVC 2011 (pp. 1-11). dundee : BMVA Press [10.5244/C.25.98].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/60416
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