局部单应约束的高精度图像自动配准方法

提出一种基于SIFT特征的抗差图像匹配算法。算法分为两个阶段:①初始匹配,综合利用SIFT特征匹配方法和基于SIFT特征尺度和方位信息的自适应归一化互相关(normalized cross correlation,NCC)方法建立初始相关,并基于几何关系一致性检测剔除误匹配;②匹配传播,在初始相关的基础上,利用自适应NCC和局部单应约束进行匹配传播,迭代产生更多的匹配点并采用几何关系一致性检测剔除可能的误匹配。初始单应采用最小二乘匹配方法估计得到,并采用自适应NCC为其提供良好的初始值。与现有的基于SIFT特征的图像配准方法相比,算法在抗几何变形和配准精度等方面具有优越性。

An Automated Image Registration Method with High Accuracy Based on Local Homography Constraint

A robust image registration algorithm is proposed,which includes the following two stages:①initial matching,SIFT matching method and the normalized cross correlation(NCC) metric modified with adaptive scale and orientation of SIFT features are proposed to find good initial matches,and the geometric consistency check is used to identify false matches;②matching propagation,a robust matching propagation using adaptive NCC and local homography constraint starts from the initial correspondences established in the first phase,and the geometrical consistency check is used simultaneously to eliminate the incorrect matches.By using matching propagation,control points used to image registration can be obtained as many as possible.Initial local homography is estimated using least squares matching algorithm and the initial values of unknown parameters needed for it is provided by adaptive NCC method.Compared to existing point-based image registration methods,the proposed algorithm has better performance in terms of registration accuracy and robustness to geometric deformations within images.