Fast Shape Matching Using a Hybrid Model

A hybrid model is proposed to finish image shape matching from coarse to fine, which is composed of three parts: rough matching, accurate matching, and optimum matching search. According to the partial Hausdorff distance, a fast strategy for rough matching and a new improved partial Hausdorff distance for accurate matching are presented as the measures of the degree of shape similarity between the template and images. At the same time, a new genetic algorithm based on fuzzy logic, which can adaptively regulate the probabilities of crossover and mutation, is used to search the optimum shape matching quickly. The experimental results show that the model achieves the shape matching with higher speed and precision compared with the traditional matching algorithms and can be used in real-time image matching and pattern recognition.