Fast RANSAC with Preview Model Parameters Evaluation
Fast RANSAC with Preview Model Parameters Evaluation
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DOI：
10.1360/jos161431
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
Chen Fu-xing;Wang Run-sheng
中科院分区：
文献类型：
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作者：
Chen Fu-xing;Wang Run-sheng
随机抽样一致性算法MNSAC（random sample consensus）是在计算机视觉领域内应用最广泛的Robust估计算法之一，但是MNSAC算法计算效率较低.该算法具有较好的鲁棒性.提出了一种预测模型参数评估RANSAC算法（PERANSAC）：在RANSAC算法的基础上增加了预测模型参数评估的选择。在保证解的置信度与RANSAC相同的情况下，在预评估选择中丢弃了大量从污染样本中获得的错误模型参数。PERANSAC算法在合成数据和真实图像上的实验结果表明，该算法在速度上有了明显的提高，且求解结果与RANSAC算法相同。
RANSAC algorithm is one of the most widely used robust estimator in the field of computer vision, but, it’s efficiency is low. The paper gives a preview model parameters evaluation RANSAC algorithm (PERANSAC): a preview model parameters evaluation selection is added to the RANSAC algorithm. With guaranteeing the same confidence of the solution as RANSAC, a very large number of erroneous model parameters obtained from the contaminated samples are discarded in the preview evaluation selection. PERANSAC algorithm is evaluated on both synthetic data and real-world images, a significant increase in speed is shown, and the solutions are the same as RANSAC’s.