在噪声图象中如何有效地提取边界是图象分析领域中的难点。启发式搜索的方法常常用于提取边界，但是，这种方法由于采用固定的起始点、固定的引导度量以及对图象仅进行一次性搜索，对噪声往往很敏感，为此提出了一种随机启发式搜索算法，该方法随机地选取起始点，并依照引导度量的概率反复地进行随机搜索获得各种可能的边界轨迹，然后进行各搜索轨迹的积累自增强，最后根据自增强积累统计结果获得边界。大量的实验结果证明，在噪声图象中，随机启发式搜索方法可以在提取出有意义边界的同时有效地抑制噪声。

Edges of objects often provide important features in pattern recognition application. How to extract the edge effectively in noise image is a difficult problem in the field of image analysis. Heuristic search algorithm is often used to extract edge, but because this method utilizes the fixed start points, fixed guide measurement and processes one off search, it is often very sensitive to noise. In this paper, a stochastic heuristic search method is presented. It firstly uses repetitive random searches to obtain various possible independent edge trajectories, then self reinforces and accumulates the search trajectories respectively, at last, extracts the edges relying on the result of the accumulation of self reinforcement. Lots of experiments show that our method can extract edges effectively and suppress the noise at the same time. Comparing with heuristic search algorithm, we find that our method is superior.