Randomized RANSAC with sequential probability ratio test
Randomized RANSAC with sequential probability ratio test
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DOI：
10.1109/iccv.2005.198
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发表时间：
2005-10
期刊：
影响因子：
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通讯作者：
Jiri Matas;Ondřej Chum
中科院分区：
文献类型：
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作者：
Jiri Matas;Ondřej Chum
提出了一种RANSAC的随机模型验证策略。所提出的方法发现，像RANSAC，是最佳的解决方案与用户可控的概率n。一个可证明的最佳模型验证策略的情况下，被离群值污染的数据是已知的，即该算法是最快的可能（平均）的所有随机RANSAC算法，保证1 - n的置信度的解决方案。最优性的推导是基于Wald的序贯决策理论。本文介绍了一种不需要先验知识的随机抽样随机抽样算法，该算法具有接近最优策略的结果。我们的实验表明，在标准的测试数据的方法是2至10倍的速度比标准的RANSAC和高达4倍的速度比以前公布的方法
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