# Detection of ‘Orange Skin’ Type Surface Defects in Furniture Elements with the Use of Textural Features

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$300×300 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.
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Communication dans un congrès
Khalid Saeed; Władysław Homenda; Rituparna Chaki. 16th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Jun 2017, Bialystok, Poland. Springer International Publishing, Lecture Notes in Computer Science, LNCS-10244, pp.402-411, 2017, Computer Information Systems and Industrial Management. 〈10.1007/978-3-319-59105-6_34〉
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Soumis le : mardi 5 décembre 2017 - 14:56:38
Dernière modification le : mercredi 6 décembre 2017 - 01:21:02

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Michał Kruk, Bartosz Świderski, Katarzyna Śmietańska, Jarosław Kurek, Leszek Chmielewski, et al.. Detection of ‘Orange Skin’ Type Surface Defects in Furniture Elements with the Use of Textural Features. Khalid Saeed; Władysław Homenda; Rituparna Chaki. 16th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Jun 2017, Bialystok, Poland. Springer International Publishing, Lecture Notes in Computer Science, LNCS-10244, pp.402-411, 2017, Computer Information Systems and Industrial Management. 〈10.1007/978-3-319-59105-6_34〉. 〈hal-01656207〉

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