A new method to solve rotated template matching using metaheuristic algorithms and the structural similarity index

Template matching (TM) is a technique used in computer vision that helps us to identify small pieces of a predefined sub image known as template and a corresponding area inside a large image. There are several approaches in the literature that solve this problem. In spite of their acceptable results, the existing methods fail to detect templates when they are rotated in a large image. On the other hand, the structural similarity (SSIM) index is a resemblance model that allows us to evaluate the affinity between two images. Different from other similarity indexes, SSIM considers important structural information while also incorporating essential elements such as luminance and contrast terms. This paper presents a new approach for template matching when an image is rotated. Our method considers the complete process as an optimization problem where a metaheuristic algorithm is used to find the best resemblance between the template and its rotated or unrotated image. In order to obtain a robust detection, our approach considers the use of the SSIM index as an objective function. Although metaheuristic approaches produce interesting results, there is not an ideal algorithm that can competitively solve all problems. Under such conditions, our study analyzes the performance of five different metaheuristic techniques for solving the problem of TM. In the comparison, several complicated computer experiments have been conducted considering different performance indexes. The results show that the proposed technique solves the problem of TM competitively for rotated instances in terms of accuracy and robustness.