| 摘要: |
| 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.提出一种基于模型参数预检验的快速MNSAC算法(preview model parameters evaluation MNSAC,简称PEMNSAC).算法在RANSAC算法模型参数检验前,加入预检验过程,在保证计算结果置信概率不变的前提下,过滤掉大量偏差较大的模型参数,提高了MNSAC算法的整体效率.模拟数据和真实图像数据实验结果表明,此算法和MNSAC算法计算精度保持一致,计算速度高于MNSAC算法. |
| 关键词: MNSAC PEMNSAC 鲁棒性 基础矩阵 LmedS估计 outliers inliers |
| DOI： |
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| Fast RANSAC with Preview Model Parameters Evaluation |
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CHEN Fu-Xing,WANG Run-Sheng
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| Abstract: |
| 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. |
| Key words: RANSAC PERANSAC robust fundamental matrix LmedS estimation outliers inliers |
