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.