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IJMLC 2020 Vol.10(2): 368-373 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.2.944

Comparison of Computing Performance of Image Processing on Different HW Platforms

Jakub Kolarik, Radek Martinek, Jakub Stefansky, Petr Bilik, and Jan Nedoma

Abstract—The work describes the evaluation of selected platforms in computing performance on a defined task. The work includes a description of the individual platforms and their hardware equipment. The chosen representatives are from categories of personal computers, embedded devices and industrial controller with on board FPGA. The evaluation of selected platforms is executed by the rising difficulty of given problem by changing the size of input data. In this case, it is the resolution of the image used by the Canny edge detecting algorithm. The result of this work is the relative comparison of the platforms, even with the increase in the volume of data processed by the algorithm. This experiment can be used to simplify architecture and hardware selection in practical applications due to presented performance in account of time complexity of given task.

Index Terms—Image processing, LabVIEW, FPGA, cRIO.

Jakub Kolarik, Radek Martinek, Jakub Stefansky, and Petr Bilik are with the Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSBā€“Technical University of Ostrava, Ostrava, Czech Republic (e-mail: jakub.kolarik@vsb.cz, {jakub.kolarik, radek.martinek, jakub.stefansky, petr.bilik}@vsb.cz).
Jan Nedoma is with the Department of Telecommunications, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, Ostrava, Czech Republic (e-mail: jan.nedoma@vsb.cz).

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Cite: Jakub Kolarik, Radek Martinek, Jakub Stefansky, Petr Bilik, and Jan Nedoma, "Comparison of Computing Performance of Image Processing on Different HW Platforms," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 368-373, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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