Adaptive Template Matching Based on Improved Ant Colony Optimization

Image matching is a basic and crucial process for imagine processing. Ant colony optimization (ACO) is a bio-inspired optimization algorithm, it has strong robustness and easy to combine with other problems. However, the basic ACO algorithm has disadvantages of stagnation, and easy to fall into local best. A novel approach to the adaptive template matching based on an improved ACO algorithm has been proposed in this paper, and coarse-fine two-stage searching methods to effectively solve the problem of finding the peak point of the correlation functions accurately. An improved ACO model is proposed to search in the coarse searching stage to decrease the time for image matching process. Then, the position of the template image in the matching image can be found under retaining a certain precision in the fine searching stage. Series simulation experiments have demonstrated the feasibility and effectiveness of the proposed approach.