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Locally Optimized RANSAC

2003; Springer Science+Business Media; Linguagem: Inglês

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

1611-3349

Ondřej Chum, Jǐŕı Matas, Josef Kittler,

Advanced Image and Video Retrieval Techniques

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.