Abstract
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.
The authors were supported by the European Union under projects IST-2001-32184,ICA 1-CT-2000-70002 and by the Czech Ministry of Education under project LN00B096 and by the Czech Technical University under project CTU0306013. The images for experiments A,B, and E were kindly provided by T. Tuytelaars (VISICS, K.U.Leuven), C by M. Pollefeys (VISICS, K.U.Leuven), and E by K. Mikolajczyk (INRIA Rhône-Alpes).
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Chum, O., Matas, J., Kittler, J. (2003). Locally Optimized RANSAC. In: Michaelis, B., Krell, G. (eds) Pattern Recognition. DAGM 2003. Lecture Notes in Computer Science, vol 2781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45243-0_31
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DOI: https://doi.org/10.1007/978-3-540-45243-0_31
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