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Massively Parallel Change Detection with Application to Visual Quality Control

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Artificial Intelligence and Soft Computing (ICAISC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9119))

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Abstract

Our aim in this paper is to extend the results on parallel change detection recently discussed in [15], where such a detector has been proposed. Here, we emphasize its adaptive abilities to follow changing background and relax some theoretical assumptions on random errors, extending possible applications of the detector. We also discuss its implementation in NVidia CUDA technology and provide results of its extensive testing when applied to copper visual quality control, which is a challenge due to the need for massively parallel calculations in real-time.

An Erratum for this chapter can be found at http://dx.doi.org/10.1007/978-3-319-19324-3_72

An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-19324-3_72

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Author information

Authors and Affiliations

  1. Department of Automatic Control and Mechatronics, Faculty of Electronics, Wrocław University of Technology, Wrocław, Poland

    Ewaryst Rafajłowicz & Karol Niżyński

Authors
  1. Ewaryst Rafajłowicz
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  2. Karol Niżyński
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Corresponding author

Correspondence to Ewaryst Rafajłowicz .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafal Scherer

  4. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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Rafajłowicz, E., Niżyński, K. (2015). Massively Parallel Change Detection with Application to Visual Quality Control. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_55

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  • DOI: https://doi.org/10.1007/978-3-319-19324-3_55

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19323-6

  • Online ISBN: 978-3-319-19324-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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