在基于特征的图像配准中,针对基于边缘的Hausdorff距离计算效率低的问题,提出用能表示图像空间结构的特征点来进行Hausdorff距离计算的方法.该方法是通过检测图像中的特征点来减少匹配点集中点的数量,实验证明了该方法的有效性,以及在计算和匹配时间上要优于基于边缘特征的Hausdorff距离计算方法.针对稀疏特征点集的特点,提出了改进Hausdorff距离,该距离通过改进部分Hausdorff距离使其更加适用于稀疏点集的距离计算,实验表明该距离在抗噪等方面优于其他Hausdorff距离.%In the image registration based on feature, aiming at the inefficient of Hausdorff distance calculations based on the edge of images, a new method is proposed to calculate the Hausdorff distance in use of key point features that could show the space structure of images. This method is to reduce the quantity of matching point sets by detecting the odd point of images. The experimental results demonstrate the validity of this method, and it is superior in calculating and matching Hausdorff distance to that one based on edge. Aiming at the characters of sparse key point sets, an improved Hausdorff distance is proposed. This Hausdorff distance is more perfect for calculating the distance of sparse key point sets. The experiment shows this distance is better in matching the noised image than the other Hausdorff distance.
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