目标定位是机器视觉领域的重要问题。针对传统方法受背景干扰点影响较为严重的问题，提出一种基于边界强度的改进型Hausdorff 距离用于目标定位。该方法在边界提取算法的基础上，利用边界长度、边界梯度和边界闭合度计算目标图像所提取边界的权值，并结合加权Hausdorff 距离建立定位匹配函数。为避免定位寻优过程陷入局部极小点，采用多变量相关的分布估计算法，利用概率模型描述匹配参数之间的相互关系，并以此模型产生优势样本，实现多维目标的准确定位。实验表明，该方法在目标定位的精确性和快速性方面都要优于传统算法。

Object location is a principal problem in the field of machine vision. In order to eliminate the negative effects of noises, an improved Hausdorff distance based on edge-strength was proposed for object location. Edge length, edge gradient and edge closure were used for edge weights computation, and a location function was then established by adopting weighted Hausdorff distance. Distribution estimation algorithm concerning related multi-variables was used to avoid local minimum during optimization, which described the relationship between matching parameters with a probability model. The tasks of location were realized by probability model evolution. Experiments show that the algorithm outperforms other approaches in both accuracy and rapidity.