基于特征距离与内点的随机抽样一致性算法

张岩; 孙世宇; 胡永江; 李建增; 范聪

doi:10.11999/JEIT170703

基于特征距离与内点的随机抽样一致性算法

doi: 10.11999/JEIT170703

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- 被引次数: 0
出版历程
- 收稿日期: 2017-07-17
- 修回日期: 2017-12-13
- 刊出日期: 2018-04-19

Random Sample Consensus Algorithm Based on Feature Distance and Inliers

摘要

摘要: 为了提高随机抽样一致性算法在特征匹配中的执行速度，该文提出一种基于特征距离与内点的随机抽样一致性算法。首先提出基于特征距离的先验概率引导方法，来提高算法每步循环找到正确模型的概率。其次提出基于样本集与内点的随机抽样及计算方法，来提高算法的收敛速度。最后提出基于最大值不变的循环跳出方法，来满足所提循环方法的跳出，同时提高运行效率。通过理论证明与实验验证，该文提出的算法在保证算法鲁棒性的同时，提高了执行速度。
- 模型估计 /
- 随机抽样一致性 /
- 特征距离 /
- 内点抽样 /
- 单应性矩阵
Abstract: In order to improve the operation efficiency of the RANdom SAmple Consensus (RANSAC) in feature registration, the Random Sample Consensus based on Feature Distance and Inliers (RSCFDI) is proposed. Firstly, the priori probability guidance method based on feature distance is proposed for increasing the probability of searching correct model in each loop. Then, to increase the convergence rate, the random sampling and calculation method based on sample set and inliers is adopted. Finally, the jumping out loop based on unchanged maximum is proposed to fit the proposed loop breaking, and the execution speed is elevated. The theoretical proof and experimental results show that the RSCFDI ensures the robustness of the algorithm, and the operation efficiency is improved.
- Model estimation /
- RANdom SAmple Consensus (RANSAC) /
- Feature distance /
- Inliers sample /
- Homograph matrix

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