Evolutionary Computation And Its Application In Image Matching

Evolutionary Computation is an important branch of artificial intelligence research that is given broad attention in recent years; it is also a main part of intelligent information processing as well. As an optimization algorithm based on the theory of biologic evolution, the most outstanding advantage of Evolutionary Computation is its strong global optimizing capability comparing with other optimization algorithms. Image Matching is a very important part in the course of image processing and pattern recognition. But when there are several kinds of difference between the matching image model and the matched image, such as in lightness, zoom or in angle, the traditional matching algorithms would not be able to obtain satisfying matching results. Aiming at this problem, we put forward an image-matching algorithm based on Evolutionary Algorithms. In our algorithm, we regard the image-matching problem as another kind of optimization problem that is looking for a most suitable matching point in matched image. Then we use Evolutionary Algorithms to solve the problem effectively.Main work of this paper is as follows:1. The paper researches into the most likely application range of Genetic Algorithms and Evolution Strategy. In the experiments, we also analyze the advantages and disadvantages of real coding and binary coding used by Genetic Algorithms.2. Aiming at the two kind of classic selective methods of Evolution Strategy, selective method and selective method, we use experiments tocompare and analyze their propriety and the way to select their parameters, and .3. Based on above work, this paper puts forward an image-matching algorithm based on Evolutionary Computation. In the algorithm, we look the image-matching problem as a kind of optimization problem that finds a most suitable matching point in matched image. Then we use the strong global optimization capability of Evolutionary Computation to solve the complicated matching problem. The results of the experiments show that, when there are different kinds of differences in lightness, zoom and angle between matching model and matched image, this kind of optimization algorithm based on Evolutionary Computation can solve the problem effectively. So our work can be seen as a good try of Evolutionary Computation for more application in the field of image processing.