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Obtaining 2D Surface Characteristics from Specular Surfaces

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Pattern Recognition (GCPR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8753))

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Abstract

Today’s surface appearance measures often ignore the inherent two-dimensionality. This paper proposes a method to acquire and assess the appearance of larger specular surfaces in 2D. First, we describe a deflectometric setup to obtain a gradient field of the surface microstructure. Hence, we propose an areal measure based on the angular power spectrum, as defined in ISO 25178, to characterize the waviness of coated surfaces in relevant scales. To verify the validity of this measure, we compare it with an 1D industry standard appearance measurement system (wave-scan). While our method shows the same characteristics when mapped to the wave-scan values, we observed differences between both systems. These are mainly caused by the different measurement principles and the resulting information of the surface.

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Author information

Authors and Affiliations

  1. Vision and Fusion Laboratory, Karlsruhe Institute of Technology, Adenauerring 4, 76131, Karlsruhe, Germany

    Mathias Ziebarth, Markus Vogelbacher & Sabine Olawsky

  2. Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, Fraunhoferstr. 1, 76131, Karlsruhe, Germany

    Jürgen Beyerer

Authors
  1. Mathias Ziebarth
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  2. Markus Vogelbacher
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  3. Sabine Olawsky
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  4. Jürgen Beyerer
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Corresponding author

Correspondence to Mathias Ziebarth .

Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Münster, Münster, Germany

    Xiaoyi Jiang

  2. Computer Science Department 5, University of Erlangen-Nürnberg, Erlangen, Germany

    Joachim Hornegger

  3. Department of Computer Science, University of Kiel, Kiel, Germany

    Reinhard Koch

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© 2014 Springer International Publishing Switzerland

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Ziebarth, M., Vogelbacher, M., Olawsky, S., Beyerer, J. (2014). Obtaining 2D Surface Characteristics from Specular Surfaces. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_57

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  • DOI: https://doi.org/10.1007/978-3-319-11752-2_57

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11751-5

  • Online ISBN: 978-3-319-11752-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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