Jirí Matas and Ondrej Chum. Randomized ransac with sequential probability ratio test. In Songde Ma and Heung-Yeung Shum, editors, Proc. IEEE International Conference on Computer Vision (ICCV), volume II, pages 1727-1732, New York, USA, October 2005. IEEE Computer Society Press.
A randomized model verification strategy for RANSAC is presented. The proposed method finds, like RANSAC, a solution that is optimal with user-controllable probability n. A provably optimal model verification strategy is designed for the situation when the contamination of data by outliers is known, i.e. the algorithm is the fastest possible (on average) of all randomized RANSAC algorithms guaranteeing 1 - n confidence in the solution. The derivation of the optimality property is based on Wald's theory of sequential decision making. The R-RANSAC with SPRT which does not require the a priori knowledge of the fraction of outliers and has results close to the optimal strategy is introduced. We show experimentally that on standard test data the method is 2 to 10 times faster than the standard RANSAC and up to 4 times faster than previously published methods.
