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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
