本文提出了一种结合Harris与SIFT算子的快速图像配准方法。首先, 对Harris算法进行两方面的改进: 一是构建高斯尺度空间, 提取具有尺度不变性的角点特征; 二是采用Forsnter算子对提取的角点精定位, 提高配准精度。然后, 利用SIFT算子的特征描述方法描述提取到的特征点, 通过随机kd树算法对两幅影像的特征点进行匹配。最后采用RANSAC算法对匹配点对进行提纯, 并通过最小二乘法估计两幅影像间的空间变换单应矩阵, 完成图像配准。实验结果表明: 本文方法在基本保持配准精度的同时, 在配准过程的时间消耗上比标准SIFT算法减少了64%。

A new method for fast image registration based on improved Harris-Sift algorithm is proposed. Firstly, classic Harris algorithm is improved by building Gaussian scale space to extract scale invariant Harris corners and they are refined to sub-pixel corners using Forsnter algorithm. Then the SIFT descriptor is utilized to characterize those feature points and the matching procedure is carried out via randomized kd trees. At last, RANSAC is used to remove wrong matches and the optimal transform parameters are estimated using the least square method to accomplish the image registration process. The experimental results demonstrate that compared with the classic SIFT algorithm the proposed method decreases the cost time of the registration procedure mostly by 64% while almost keeping the same performance.