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Quality Evaluation of 3D Printed Surfaces Based on HOG Features

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Computer Vision and Graphics (ICCVG 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11114))

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Abstract

The main purpose of the visual quality assessment of 3D prints is the detection of surface distortions which can be made using various approaches. Nevertheless, a reliable classification of 3D printed samples into low and high quality ones can be troublesome, especially assuming the unknown color of the filament. Such a classification can be efficiently conducted using the approach based on the Histogram of Oriented Gradients (HOG) proposed in this paper. Obtained results are very promising and allow proper classification for the most of the tested samples, especially for some of the most typical distortions.

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Authors and Affiliations

  1. Department of Signal Processing and Multimedia Engineering, Faculty of Electrical Engineering, West Pomeranian University of Technology, Szczecin, 26 Kwietnia 10, 71-126, Szczecin, Poland

    Piotr Lech, Jarosław Fastowicz & Krzysztof Okarma

Authors
  1. Piotr Lech
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  2. Jarosław Fastowicz
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  3. Krzysztof Okarma
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Corresponding author

Correspondence to Krzysztof Okarma .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland

    Leszek J. Chmielewski

  2. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland

    Ryszard Kozera

  3. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland

    Arkadiusz Orłowski

  4. Institute of Computer Science, Silesian University of Technology, Gliwice, Poland

    Konrad Wojciechowski

  5. Technion, Israel Institute of Technology, Haifa, Israel

    Alfred M. Bruckstein

  6. University of Groningen, Groningen, The Netherlands

    Nicolai Petkov

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Lech, P., Fastowicz, J., Okarma, K. (2018). Quality Evaluation of 3D Printed Surfaces Based on HOG Features. In: Chmielewski, L., Kozera, R., Orłowski, A., Wojciechowski, K., Bruckstein, A., Petkov, N. (eds) Computer Vision and Graphics. ICCVG 2018. Lecture Notes in Computer Science(), vol 11114. Springer, Cham. https://doi.org/10.1007/978-3-030-00692-1_18

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  • DOI: https://doi.org/10.1007/978-3-030-00692-1_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00691-4

  • Online ISBN: 978-3-030-00692-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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