在计算机视觉检测中,常常需要将两幅图象在空间上配准,以便进行后续的检测过程.该文提出将Hausdorff距离作为物体轮廓相似性的测度,并用遗传算法进行最佳形状匹配的快速搜索,根据遗传搜索的结果再进行一次线性搜索,从而提高解的精度.实验结果证明了该方法能快速、精确地对两幅2D形状进行匹配.

Matching between two images is often needed in automated visual inspection. Template matching, which is the most principle approach for shape match, is time consuming in case of variation in position and rotation. In this paper, an improved algorithm for 2D shape matching based on Hausdorff Distance is proposed. Hausdorff Distance is used to measure the degree of similarity between two objects to make matching more efficiently. A high dimensional, non-diferentiable, and multi-modal objective function can be derived based on Hausdorff Distance. Although Genetic Algorithm is a powerful and attractive procedure for function optimization, the solution generated by the procedure do not guarantee to be the global optimal. A follow-up optimization scheme such as the line search method is applied, which is capable of finding the minimum value of a unimodal function over a finite search interval. Initially the non-differentiable function is solved using multi-point stochastic search, and the solution is further improved by executing a sequence of successive line searches that approach the optimal to a pre-determined precision. The experimental results show that the proposed method is capable of matching 2D shape with higher speed and precision.