Advertisement

Detection of Surface Defects of Type ‘orange skin’ in Furniture Elements with Conventional Image Processing Methods

  • Leszek J. Chmielewski
  • Arkadiusz Orłowski
  • Katarzyna Śmietańska
  • Jarosław Górski
  • Krzysztof Krajewski
  • Maciej Janowicz
  • Jacek Wilkowski
  • Krystyna Kietlińska
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9555)

Abstract

An attempt was made to differentiate between surfaces of furniture elements having the orange skin defect and those free from it. As the detectors, the directional derivative of the image intensity along the dominating light direction and the modulus of the image intensity gradient were used. The detectors were tested on series of images with the small and large light incident angles. In case of both detectors, there existed sufficiently wide ranges of thresholds for which both sensitivity and specificity were \(100\,\%\) for all the 19 images tested. The ranges of thresholds were wider for the light closer to tangential, and for the detector using the gradient modulus, than for the other cases. The optimum scale of the detectors was found different for each light conditions.

Keywords

Defect detection Quality inspection Furniture elements Orange skin Directional derivative Gradient modulus Image intensity 

References

  1. 1.
    Laszewicz, K., Górski, J.: Control charts as a tool for the management of dimensional accuracy of mechanical wood processing (in Russian). Ann. Wars. Univ. Life Sci.-SGGW, For. Wood Technol. 65, 88–92 (2008)Google Scholar
  2. 2.
    Laszewicz, K., Górski, J., Wilkowski, J.: Long-term accuracy of MDF milling process-development of adaptive control system corresponding to progression of tool wear. Eur. J. Wood Wood Prod. 71(3), 383–385 (2013)CrossRefGoogle Scholar
  3. 3.
    Chmielewski, L.J., et al.: Defect detection in furniture elements with the Hough transform applied to 3D data. In: Burduk, R., Jackowski, K., Kurzyński, M., et al. (eds.) Proceedings of 9th International Conference Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol. 403, pp. 631–640. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-26227-7_59 CrossRefGoogle Scholar
  4. 4.
    Bucur, V.: Techniques for high resolution imaging of wood structure: a review. Meas. Sci. Technol. 14(12), R91 (2003)CrossRefGoogle Scholar
  5. 5.
    Longuetaud, F., Mothe, F., et al.: Automatic knot detection and measurements from X-ray CT images of wood: a review and validation of an improved algorithm on softwood samples. Comput. Electron. Agric. 85, 77–89 (2012)CrossRefGoogle Scholar
  6. 6.
    Ilea, D.E., Whelan, P.F.: Image segmentation based on the integration of colourtexture descriptors - a review. Pattern Recogn. 44(1011), 2479–2501 (2011)CrossRefMATHGoogle Scholar
  7. 7.
    Marr, D., Hildreth, E.: Theory of edge detection. Proc. Roy. Soc. B 207, 187–217 (1980)CrossRefGoogle Scholar
  8. 8.
    Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. Int. J. Comput. Vis. 30(2), 117–156 (1998)CrossRefGoogle Scholar
  9. 9.
    Lusted, L.: Signal detectability and medical decision-making. Sci. 171(3977), 1217–1219 (1971). doi: 10.1007/978-3-662-07807-5 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Leszek J. Chmielewski
    • 1
  • Arkadiusz Orłowski
    • 1
  • Katarzyna Śmietańska
    • 2
  • Jarosław Górski
    • 2
  • Krzysztof Krajewski
    • 2
  • Maciej Janowicz
    • 1
  • Jacek Wilkowski
    • 2
  • Krystyna Kietlińska
    • 1
  1. 1.Faculty of Applied Informatics and Mathematics (WZIM)Warsaw University of Life Sciences (SGGW)WarsawPoland
  2. 2.Faculty of Wood Technology (WTD)Warsaw University of Life Sciences (SGGW)WarsawPoland

Personalised recommendations