Simulated annealing optimization in Chamfer matching

An important aspect of two-dimensional object recognition is matching. Based on a generalized distance transform method, Borgefors introduced the concept of the Chamfer scheme to two-dimensional object matching [1]. In Chamfer matching, a succesful match is obtained when a scene in a reference image (map, for example) is correctly matched to the corresponding scene in the target image (aerial image. for example). The succesful match corresponds to the lowest matching measure computed from the sum of Chamfer distances. However, if the target image has not been correctly corrected for distortions (such as perspective distortions), there may not be an ideal best fit between the reference and target images. Instead, matching measures corresponding to local minima exist, for which it is difficult to choose the correct matching measure that corresponds to the best fit. The match fit corresponding to the global minimum measure may not necessarily be the best fit as there are many sub-optimal measures that could provide equally good fits between the target and reference images. This paper advocates that aside from the Chamfer matching measure, additional considerations need to be factored in to improve on finding the true global minimum measure which corresponds to the optimal fit. In this paper, a new method has been designed to find the optimal fit between poorly-rectified target and reference images. The new approach uses simulated annealing to improve on the matching results. Simulated annealing is a powerful stochastic optimization technique for non-linear problems. In our novel technique, the energy function for simulated annealing comprises two terms: a smoothness constraint term and the Chamfer matching measure term. The high computational burden of simulated annealing is reduced by using edge information for the matching process. Results are presented to illustrate the new matching technique.