基于K-means聚类和RANSAC的图像配准算法研究

针对图像配准中特征点匹配方法存在实时性不高和精度低的问题，提出了一种基于K means聚类和RANSAC的图像配准算法。该算法根据匹配点对距离和方向特征的视差约束条件，首先利用K means聚类对匹配点对进行预处理，剔除大部分错误匹配点，然后利用RANSAC进行二次优化，实现了图像的快速和精确配准。实验结果表明，该算法不仅提高了图像配准的精确度，而且提高了图像配准的速度。

Research on the image registration algorithm based on K-means clustering and RANSAC

Aiming at the problems of less real time and low precision of feature matching in image registration, an image registration algorithm based on K means and RANSAC is proposed.Based on the parallax constraint of distance and angle features of matching point pairs,the algorithm firstly uses K means clustering to pro process matching point pairs in order to filter false matching point pairs, and secondly adopts RANSAC to optimize the matching point pairs so as to realize fast and precise image registration.The experimental results indicate that the algorithm improves both the speed and the precision of image registration.