The Application of Improved Hausdorff Distance and Genetic Algorithm in Image Matching

Effectively matching the model with the image is studied. The directed Hausdorff distance is improved to measure the degree of similarity between models and images, which can reduce the computational complexity. Genetic algorithm is used to search the interested objects. Because the genetic algorithm is a parallel and robust algorithm, the combination of genetic algorithm and improved Hausdorff distance can be used to find the global optimum results. The experimental results show that the proposed method can efficiently detect the objects that are changed in translation, rotation and scale. The method can be used in image identification and matching in practice.