采用SURF特征和局部互相关信息的图像配准算法

针对现有图像配准方法中存在的鲁棒性与配准精度难以兼容的问题，提出了一种采用SURF特征和局部互相关信息的图像配准算法。首先通过SURF特征提取方法进行初步粗配准以提升配准鲁棒性，然后利用图像中局部关键区域的互相关系数计算出单应矩阵，最后将单应矩阵应用于粗配准结果，对粗配准后的图像进行旋转变换，从而实现高精度和高鲁棒性的图像配准。实验结果表明:提出的配准方法与基于SIFT、ORB、SURF、互相关信息的图像配准方法在多组数据上进行了对比，不仅表现出了较高的配准精度和配准效率，也表现出了更优的鲁棒性。

Image registration algorithm using SURF feature and local cross-correlation information

Aiming at the problem that the robustness and registration accuracy are difficult to be compatible in the existing image registration methods, an image registration algorithm using SURF feature and local cross-correlation information was proposed. Firstly, the SURF feature extraction method was used for preliminary rough registration to improve the robustness of registration. Then, the homography matrix was calculated by using the correlation coefficient of local key areas in the image. Finally, the homography matrix was applied to the rough registered image results for rotation transformation, so as to realize high precision and robust image registration. The experimental results show that the proposed registration method is compared with the image registration method based on SIFT, ORB, SURF and cross-correlation information on multiple groups of data, which not only shows higher registration accuracy and efficiency, but also shows better robustness.