在分析Hausdorff距离特性的基础上，提出了一种两级实时景象匹配算法.与传统各种利用图像多尺度特征的多级匹配方法不同，该算法利用Hausdorff 距离特性直接在原分辨率图像上进行匹配，通过“减少匹配位置”以及“减少匹配位置相似性测度计算量”两种途径缩短匹配时间.跳跃式搜索极大地减少了参与匹配的位置数；而在每个匹配位置，只计算由特征点组成的两个点集间的LTS-HD相似性测度，非特征点不参与计算，从而大大减少了该匹配位置的相似性测度计算量.为了保证匹配准确度，采用由粗到精的两级匹配策略，第一级采用像素跳跃式全局搜索获得粗匹配点，第二级以第一级匹配为基础，在以粗匹配点为中心的δ邻域内局部遍历搜索获得精匹配点.仿真分析表明，提出的算法相比传统的遍历搜索及遗传算法耗时短且定位准确，在实时图存在严重遮挡的情况下仍能正确匹配.

By analyzing the characteristics of the HD measure,a real-time two-level scene matching algorithm is proposed.Compared with traditional image multi-scale feature decomposition matching methods,the proposed method is performed directly on the original resolution image and shortens the matching time by reducing both the number of match points and the computation complexity of similarity measure.The pixel-jump method decreases the participated match points greatly,and the computation of LTS-HD at each point is performed between sets consisted of only feature points,which efficiently simplify the computation of similarity measure.To ensure the match precision,a coarse-to-fine two-level matching strategy is adopted.In the first level,a coarse match point is obtained using pixel-jump searching through the reference image.In the second level,a point-by-point local searching is performed to get the accurate match point within the δ-neighborhood around the coarse point.Simulation results show that the proposed matching method takes less time than both the point-by-point searching and the genetic algorithm,and that the match point is correct even though the actual image is occluded severely.