Randomized RANSAC with Td,d test
Randomized RANSAC with Td,d test
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DOI：
10.1016/j.imavis.2004.02.009
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发表时间：
2004-09-01
影响因子：
4.7
通讯作者：
Chum, O
中科院分区：
文献类型：
--
作者：
Matas, J;Chum, O
许多计算机视觉算法都包含一个稳健估计步骤，即从包含大量异常值的数据集中计算模型参数。RANSAC算法可能是计算机视觉领域中使用最广泛的稳健估计器。在本文中，我们表明在广泛的条件下，如果对其假设评估步骤进行随机化，RANSAC的效率会显著提高。我们引入了一种新的RANSAC算法的随机（假设评估）版本——R - RANSAC。对于受异常值污染的模型，通常只需评估一小部分数据点，从而节省了计算量。这个想法通过一个两步评估过程来实现。我们引入了一类在数学上易于处理的样本统计预验证测试。对于这类预验证测试，我们推导出了其单个参数的最优设置的近似关系。所提出的预测试在合成数据和实际问题上都进行了评估，结果显示速度有了显著提高。(C) 2004 Elsevier B.V. 保留所有权利。
Many computer vision algorithms include a robust estimation step where model parameters are computed from a data set containing a significant proportion of outliers. The RANSAC algorithm is possibly the most widely used robust estimator in the field of computer vision. In the paper we show that under a broad range of conditions, RANSAC efficiency is significantly improved if its hypothesis evaluation step is randomized.A new randomized (hypothesis evaluation) version of the RANSAC algorithm, R-RANSAC, is introduced. Computational savings are achieved by typically evaluating only a fraction of data points for models contaminated with outliers. The idea is implemented in a two-step evaluation procedure. A mathematically tractable class of statistical preverification test of samples is introduced. For this class of preverification test we derive an approximate relation for the optimal setting of its single parameter. The proposed pre-test is evaluated on both synthetic data and real-world problems and a significant increase in speed is shown. (C) 2004 Elsevier B.V. All rights reserved.