基于Hausdorff距离图象配准方法研究

图象配准是图象融合的一个重要步骤，为此提出了一种自动图象配准算法，该算法从两幅待配准的图象中分别抽取特征点，然后选用Hausdorff距离对两特征点集进行匹配，得到点集间的仿射变换，从而实现图象的自动配准，此算法以特征点而不是物体边缘计算仿射变换，大大降低了计算Hausdorff距离的运算量；同时，基于Hausdorff距离的图象匹配只需要点集之间的对应，而无须点与点的对应，因而可以使用于存在较大物体形变的情况，即完成两幅差异较大图象的配准，实验结果证明了算法的有效性。

Image Registration Based on Hausdorff Distance

Image registration is an important step in image fusion. In this paper, a new automatic image registration method is presented. First, a small number of feature points are extracted in both images using a Gabor wavelet feature detector. Then, these feature points are matched and the affine transformation between the two images is obtained through a matching technique based on the Hausdorff distance. We choose feature points instead of edges of objects to search for the affine transformation so that the computation load can be decreased largely. On the same time, because the Hausdorff distance is a measure defined between two point sets and does not require to establish an explicit points correspondence between images, it can tolerate errors introduced by the presence of outlier points (noises) as well as the absence of some missing points. Consequently, this registration method can be applied to images with large misalignment. Experiments with synthetic and real images show that this algorithm is efficient.