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- W2616217103 abstract "The accuracy of detecting the orange skin surface defect in lacquered furniture elements was tested. Textural features and an SVM classifier were used. Features were selected from a set of 50 features with the bottom-up feature selection strategy driven by the Fisher measure. The features selected were the Kolmogorow-Smirnow-based features, some of the Hilbert curve-based features, some of the maximum subregions features and also some of the thresholding-based features. The Otsu thresholding and percolation-based features were all rejected. The images of size (300,times ,300) pixels cut from the original, larger images were treated as objects. There were three quality classes: very good, good and bad. In the cross-validation process where the testing sets consisted of 90 and the training sets of 910 objects the accuracies ranged from 90% to 98% and the average accuracy was 94%. The tests revealed that more research should be done on the choice of features for this problem." @default.
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- W2616217103 date "2017-01-01" @default.
- W2616217103 modified "2023-10-15" @default.
- W2616217103 title "Detection of ‘Orange Skin’ Type Surface Defects in Furniture Elements with the Use of Textural Features" @default.
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- W2616217103 doi "https://doi.org/10.1007/978-3-319-59105-6_34" @default.
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