随机抽样一致性(RANSAC)算法广泛应用于图像匹配领域,帮助图像匹配算法筛选并剔除误匹配点对,提高图像匹配的精度。经典的RANSAC算法存在计算数据量大、运算速度慢、样本适应性差等问题,无法满足图像匹配实时性和样本多变性的需求。针对以上问题,提出了一种基于自适应预检验和迭代阈值自适应相结合的改进RANSAC算法,并给出了具体的设计思路和工作原理。通过实验验证发现,该算法相比于经典的RANSAC算法,运算速度和样本适应性方面都有大幅提升。

Random Sample Consensus (RANSAC) algorithm is widely used in the field of image matching, which helps the image matching algorithm to filter and eliminate mismatched pairs and improve the accuracy of image matching.The classical RANSAC algorithm has the problems such as large amount of computation, slow operation speed, and poor sample adaptability, which cannot meet the requirements on real-time performance and sample variability in image matching.Aiming at the above problems, an improved RANSAC algorithm is proposed based on adaptive pre-test and adaptive iterative threshold,and the working principles are given.The experimental results show that the proposed algorithm has a significant improvement in computation speed and sample adaptability compared with the classical RANSAC algorithm.