基于邻域灰度信息的Hausdorff距离图像匹配方法

针对可见光与红外图像由于成像机理不同引起的图像灰度值差异大、边缘轮廓不一致、传统基于灰度和基于特征的匹配方法匹配概率不高等问题，在分析了各种Hausdorff距离算法的前提下，引入可见光与红外图像的灰度信息，提出一种基于邻域灰度信息Hausdorff距离的图像匹配方法。该方法在计算图像边缘特征点相似性的基础上，增加了邻域归一化灰度方差计算，有效解决了由于边缘差异引起的Hausdorff距离算法对可见光/红外图像匹配概率不高的问题。经可见光与红外图像匹配的仿真实验表明，在各种条件下，该算法与传统Hausdorff距离算法相比，有效提高了在不同光照下图像的匹配效率以及对噪声的抗干扰性能。

Image matching method based on normalized grayscale variance Hausdorff distance

As for the large differences between the visual and infrared images in gray value caused by different imaging mechanism, inconsistent contour, the low matching probability of traditional matching methods based on gray or feature, the gray information of visual and infrared images was introduced after researching a variety of Hausdorfff Distance (HD) algorithms. Image matching method based on the neighbor grayscale information Hausdorfff distance was proposed. Based on the calculation of the similarity of edge feature points, the calculation of image normalized grayscale variance was added into this method, which effectively solved the low probability problem caused by different edge of visual/infrared image in Hausdorff distance matching algorithms. The simulation results of visual and infrared images matching show that under various conditions, compared with the conventional Hausdorff distance method, the proposed algorithm effectively improves matching effect under different light conditions and anti-jamming of noise.