Epipolar Geometry Estimation Using Improved LO-RANSAC

Abstract:

The estimation of the epipolar geometry is of great interest for a number of computer vision and robotics tasks, and which is especially difficult when the putative correspondences include a low percentage of inliers correspondences or a large subset of the inliers is consistent with a degenerate configuration of the epipolar geometry that is totally incorrect. The Random Sample Consensus (RANSAC) algorithm is a popular tool for robust estimation, primarily due to its ability to tolerate a tremendous fraction of outliers. In this paper, we propose an approach for improve of locally optimized RANSAC (LO-RANSAC) that has the benefit of offering fast and accurate RANSAC. The resulting algorithm when tested on real images with or without degenerate configurations gives quality estimations and achieves significant speedups compared to the LO-RANSAC algorithms.

• Periodical: Advanced Materials Research (Volume 213)
• Pages: 255-259
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.213.255
• Online since: February 2011
• Authors: Jun Zhou
• Keywords: Computer Vision, Epipolar Geometry, Robust Estimation

References

[1] R. Hartley and A. Zisserman: Multiple View Geometry in Computer Vision, Cambridge Univ. Press (2003).

[2] M. Fischler and R. Bolles: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography, Comm. ACM, vol. 24(1981), no. 6, p.381.

DOI: 10.1145/358669.358692

[3] P.H.S. Torr and A. Zisserman: MLESAC: A new robust estimator with application to estimating image geometry, Computer Vision and Image Understanding (2000), vol. 78, p.138.

DOI: 10.1006/cviu.1999.0832

[4] P.H.S. Torr: Bayesian model estimation and selection for epipolar geometry and generic manifold fitting, International Journal of Computer Vision, vol. 50(2002), p.35.

[5] R. Subbarao and P. Meer: Subspace estimation using projection-based M-estimator over grassmann manifolds, In Proceedings of the 9th European Conference on Computer Vision (ECCV2006), p.301.

DOI: 10.1007/11744023_24

[6] O. Chum, J. Matas and J. Kittler: Locally Optimized RANSAC, Proc. German Pattern Recognition Symp (2003), p.236.

DOI: 10.1007/978-3-540-45243-0_31

[7] B. Tordoff and D. Murray: Guided-mlesac: Faster image transform estimation by using matching priors, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27(2005), no. 10, p.1523.

DOI: 10.1109/tpami.2005.199

[8] O. Chum and J. Matas: Matching with PROSAC: Progressive Sample Consensus, Proc. IEEE Conf. Computer Vision and Pattern Recognition (2005), p.220.

DOI: 10.1109/cvpr.2005.221

[9] O. Chum and J. Matas: Randomized RANSAC with Td; d test, In Preceedings of the 13th British Machine Vision Conference (BMVC2002), p.448.

[10] J. Matas and O. Chum: Randomized RANSAC with sequential probability ratio test, In Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV2005).

DOI: 10.1109/iccv.2005.198

[11] D. Capel: An effective bail-out test for RANSAC consensus scoring, In Proceedings of the British Machine Vision Conference (BMVC2005).

DOI: 10.5244/c.19.78

[12] D. Nister: Preemptive RANSAC for live structure and motion estimation, In Proceedings of the 9th IEEE International Conference on Computer Vision (ICCV2003), p.199.

DOI: 10.1109/iccv.2003.1238341