Locally Optimized RANSAC
A new enhancement of ransac, the locally optimized ransac (lo-ransac), is introduced. It has been observed that, to find an optimal solution (with a given probability), the number of samples drawn in ransac is significantly higher than predicted from the mathematical model. This is due to the incorrect assumption, that a model with parameters computed from an outlier-free sample is consistent with all inliers. The assumption rarely holds in practice. The locally optimized ransac makes no new assumptions about the data, on the contrary – it makes the above-mentioned assumption valid by applying local optimization to the solution estimated from the random sample.
The performance of the improved ransac is evaluated in a number of epipolar geometry and homography estimation experiments. Compared with standard ransac, the speed-up achieved is two to three fold and the quality of the solution (measured by the number of inliers) is increased by 10-20%. The number of samples drawn is in good agreement with theoretical predictions.
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- 1.Chum, O., Matas, J.: Randomized ransac with T(d,d) test. In: Proceedings of the British Machine Vision Conference, vol. 2, pp. 448–457 (2002)Google Scholar
- 2.Clarke, J., Carlsson, S., Zisserman, A.: Detecting and tracking linear features efficiently. In: Proc. 7th BMVC, pp. 415–424 (1996)Google Scholar
- 4.Hartley, R.: Indefence of the 8-point algorithm. In: ICCV 1995, pp. 1064–1070 (1995)Google Scholar
- 6.Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Proc. of the BMVC, vol. 1, pp. 384–393 (2002)Google Scholar
- 7.McLauchlan, P., Jaenicke, A.: Image mosaicing using sequential bundle adjustment. In: Proc. BMVC, pp. 616–662 (2000)Google Scholar
- 8.Myatt, D., Torr, P., Nasuto, S., Bishop, J., Craddock, R.: Napsac: High noise, high dimensional robust estimation - it’s in the bag. In: BMVC 2002, vol. 2, pp. 458–467 (2002)Google Scholar
- 9.Pritchett, P., Zisserman, A.: Wide baseline stereo matching. In: Proc. International Conference on Computer Vision, pp. 754–760 (1998)Google Scholar
- 10.Schaffalitzky, F., Zisserman, A.: Viewpoint invariant texture matching and wide baseline stereo. In: Proc. 8th ICCV on Vancouver, Canada (July 2001)Google Scholar
- 12.Torr, P., Zisserman, A., Maybank, S.: Robust detection of degenerate configurations while estimating the fundamental matrix. CVIU 71(3), 312–333 (1998)Google Scholar
- 13.Torr, P.H.S.: Outlier Detection and Motion Segmentation. PhD thesis, Dept. of Engineering Science, University of Oxford (1995)Google Scholar
- 15.Tuytelaars, T., Van Gool, L.: Wide baseline stereo matching based on local, affinely invariant regions. In: Proc. 11th British Machine Vision Conference (2000)Google Scholar