近邻点一致性的随机抽样一致性算法

摘要

随机抽样一致性算法是计算机视觉领域中应用广泛的多视几何图像参数估计方法之一.为提高在高噪声条件下原算法计算效率,提出基于匹配点对邻近点集合一致性的过滤方法,可提高原初始匹配点对集合的内点比例,减少使用RANSAC时寻找参数时的抽样次数.宽基线图像极线几何计算的多组实验表明:提出的算法能够保持与原RANSAC一致的结果,并有效地提高计算速度.%Random sample consensus (RANSAC) algorithm is one of the most widely used approaches in the field of computer multi-view vision. To improve the efficiency of initial matching with high noise, a new filter algorithm is proposed based on near point consistency. The new method improves the inliers ratio of initial matching set and reduces the iteration times when using RANSAC to find parameters. The experiment on wide-based pictures demonstrates that the new algorithm achieves a significant increase in speed and almost the same result compared with the classical RANSAC algorithm.