Deterministic Crowding Introducing the Distribution of Population for Template Matching.

This paper explains deterministic crowding (DC), introducing the distribution of population for template matching. We apply a simple genetic algorithm (GA) to template matching because this approach is effectively able to optimize geometric transformation parameters, such as parallel transformation, scaling, and in-plane rotation. However, since the simple GA can obtain only one global optimum, detecting multiple objects is difficult. This is not of practical use. In order to detect multiple objects, we focus on DC, which is a multimodal optimization method and able to obtain multiple global and local solutions. In DC, there is a drawback where many individuals converge to one object and, hence, some objects cannot be detected. In order to solve this problem, the proposed method introduces the distribution of population. In experiments, the proposed method, DC, and crowding are applied to template matching and compared. The results confirm that the proposed method is better when an optimal threshold, which is used to create a cluster, is set. [ABSTRACT FROM AUTHOR]