Realtime arbitrary-shaped template matching process

Template matching is one of the significant methods used in most computer vision applications. However, one disadvantage of existing template matching methods is that a template must be a rectangular image. Several solutions can be used to overcome this problem, such as feature-based, edge-based, etc., but most of them do not meet the requirements of practical applications because of their slow speed or low accuracy. In this paper, the authors propose a process for matching a template with an arbitrary shape using the idea of coarse-fine and refining interesting regions. Variety of measures can be used to provide different levels of performance for each practical situation. Experiments show that the process performs an impressive performance in average. Using the same measures, the proposed process works 5 times faster than the FFT template matching method (implemented in OpenCV), while achieving the similar accuracy. Moreover, most parts of the proposed process can be executed in parallel on multi-core CPUs or GPUs.