Textural features based on run length encoding in the classification of furniture surfaces with the orange skin defect

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Jakub Pach
Izabella Antoniuk
Leszek Chmielewski
Jarosław Górski
Michał Kruk
Jarosław Kurek
Arkadiusz Orłowski
Katarzyna Śmietańska
Bartosz Świderski
Grzegorz Wieczorek

Keywords : quality inspection, furniture surface, orange skin, textural features, run length coding, thresholded image, one nearest neighbour, leave-one-out testing

Textural features based upon thresholding and run length encoding have been successfully applied to the problem of classification of the quality of lacquered surfaces in furniture exhibiting the surface defect known as orange skin. The set of features for one surface patch consists of 12 real numbers. The classifier used was the one nearest neighbour classifier without feature selection. The classification quality was tested on 808 images 300 by 300 pixels, made under controlled, close-to-tangential lighting, with three classes: good, acceptable and bad, in close to balanced numbers. The classification accuracy was not smaller than 98% when the tested surface was not rotated with respect to the training samples, 97% for rotations up to 20 degrees and 95.5% in the worst case for arbitrary rotations.

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Pach, J., Antoniuk, I., Chmielewski, L., Górski, J., Kruk, M., Kurek, J., Orłowski, A., Śmietańska, K., Świderski, B., & Wieczorek, G. (2019). Textural features based on run length encoding in the classification of furniture surfaces with the orange skin defect. Machine Graphics and Vision, 28(1/4), 35–45. https://doi.org/10.22630/MGV.2019.28.1.4

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