Fast Affine Template Matching over Galois Field

In this paper, we address the problem of template matching under affine transformations with general images. Our approach is to search an approximate affine transformation over a binary Galois field. The benefit is that we can avoid matching with huge amount of potential transformations, because they are discretely sampled. However, a Galois field of affine transformation can still be impractical for exhaustive searching. To approach the optimum solution efficiently, we introduce a level-wise adaptive sampling (LAS) method under genetic algorithm framework. In LAS, individuals converge to the global optimum depending on a level-wise selection and crossover while the population number is decreased by a population bounding scheme. In the experiment section, we analyse our method systematically and compare it against the state-of-the-art method on an evaluation data set. The results show that our method has a higher accuracy performance with fewer matching tests.