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Testing the Limits of Detection of the ‘Orange Skin’ Defect in Furniture Elements with the HOG Features

  • Leszek J. Chmielewski
  • Arkadiusz Orłowski
  • Grzegorz Wieczorek
  • Katarzyna Śmietańska
  • Jarosław Górski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10192)

Abstract

In principle, the orange skin surface defect can be successfully detected with the use of a set of relatively simple image processing techniques. To assess the technical possibilities of classifying relatively small surfaces the Histogram of Oriented Gradients (HOG) and the Support Vector Machine were used for two sets of about 400 surface patches in each. Color, grey and binarized images were used in tests. For grey images the worst classification accuracy was 91% and for binarized images it was 99%. For color image the results were generally worse. The experiments have shown that the cell size in the HOG feature extractor should be not more than 4 by 4 pixels which corresponds to 0.12 by \(0.12\,\)mm on the object surface.

Keywords

Orange skin Orange peel Surface defect Detection Quality inspection Furniture Histogram of Oriented Gradients (HOG) 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Leszek J. Chmielewski
    • 1
  • Arkadiusz Orłowski
    • 1
  • Grzegorz Wieczorek
    • 1
  • Katarzyna Śmietańska
    • 2
  • Jarosław Górski
    • 2
  1. 1.Faculty of Applied Informatics and Mathematics – WZIMWarsaw University of Life Sciences – SGGWWarsawPoland
  2. 2.Faculty of Wood Technology – WTDWarsaw University of Life Sciences – SGGWWarsawPoland

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