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Co-Design System for Template Matching Using Dedicated Co-Processor and Cuckoo Search

Co-Design System for Template Matching Using Dedicated Co-Processor and Cuckoo Search

Alexandre de Vasconcelos Cardoso, Yuri Marchetti Tavares, Nadia Nedjah, Luiza de Macedo Mourelle
Copyright: © 2018 |Volume: 9 |Issue: 1 |Pages: 17
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781522544845|DOI: 10.4018/IJSIR.2018010104
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MLA

Cardoso, Alexandre de Vasconcelos, et al. "Co-Design System for Template Matching Using Dedicated Co-Processor and Cuckoo Search." IJSIR vol.9, no.1 2018: pp.58-74. http://doi.org/10.4018/IJSIR.2018010104

APA

Cardoso, A. D., Tavares, Y. M., Nedjah, N., & Mourelle, L. D. (2018). Co-Design System for Template Matching Using Dedicated Co-Processor and Cuckoo Search. International Journal of Swarm Intelligence Research (IJSIR), 9(1), 58-74. http://doi.org/10.4018/IJSIR.2018010104

Chicago

Cardoso, Alexandre de Vasconcelos, et al. "Co-Design System for Template Matching Using Dedicated Co-Processor and Cuckoo Search," International Journal of Swarm Intelligence Research (IJSIR) 9, no.1: 58-74. http://doi.org/10.4018/IJSIR.2018010104

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Abstract

Template matching is an important method used for object tracking, in order to find a given-pattern within a frame sequence. Pearson's Correlation Coefficient (PCC) is widely used to quantify the similarity between two images. Since this coefficient calculus is computed for each image pixel, it entails a computationally expensive process. In this article, an embedded co-design system is proposed, which implements the template matching, in order to accelerate this process. The dedicated co-processor, responsible for performing the PCC computation, is used in two configurations: serial and pipeline. Cuckoo Search (CS) is used to improve the search for the maximum correlation point of the image and the used template. The search process is implemented in software and is run by an embedded general-purpose processor. The performance results are compared to those obtained through Particle Swarm Optimization (PSO) using the same hardware approach.

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