Abstract
The surfaces of furniture elements having the orange skin surface defect were investigated in the context of selecting optimum features for surface classification. Features selected from a set of 50 features were considered. Seven feature selection methods were used. The results of these selections were aggregated and found consistently positive for some of the features. Among them were primarily the features based on local adaptive thresholding and on Hilbert curves used to evaluate the image brightness variability. These types of features should be investigated further in order to find the features with more significance in the problem of surface quality inspection. The groups of features which appeared least profitable in the analysis were the two features based on percolation, and the one based on Otsu global thresholding.
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- 1.
This does not concern CFS, where features are not sequenced; in this method, the following features were selected: \(\{ 2, 3, 13, 14, 23, 24, 28, 34, 39, 40, 41, 43, 45, 47\}\).
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Świderski, B. et al. (2018). Feature Selection for ‘Orange Skin’ Type Surface Defect in Furniture Elements. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10842. Springer, Cham. https://doi.org/10.1007/978-3-319-91262-2_8
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