Abstract
The template matching is an important technique used in pattern recognition. The goal is find a given pattern, from a prescribed model, in a frame sequence. In order to evaluate the similarity of two images, the Pearsons Correlation Coefficient (PCC) is widely used. This coefficient is calculated for each of the image pixels, which entails a computationally very expensive operation. This paper proposes the implementation of Template Matching using the PCC based method together with Particle Swarm Optimization as an embedded system. This approach allows for a great versatility to use this kind of system in portable equipment. The results indicate that PSO is up to 158x faster than the brute force exhausted search. So, the thus obtained co-design with PCC computation implemented in hardware, while the PSO process in software, is a viable way to achieve real time template matching, which is a pre-requisite in real-word applications.
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Tavares, Y.M., Nedjah, N., de Macedo Mourelle, L. (2016). Embedded Implementation of Template Matching Using Correlation and Particle Swarm Optimization. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9787. Springer, Cham. https://doi.org/10.1007/978-3-319-42108-7_41
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DOI: https://doi.org/10.1007/978-3-319-42108-7_41
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