%0 Conference Proceedings
%T Detection of ‘Orange Skin’ Type Surface Defects in Furniture Elements with the Use of Textural Features
%+ Warsaw University of Life Sciences (SGGW)
%A Kruk, Michał
%A Świderski, Bartosz
%A Śmietańska, Katarzyna
%A Kurek, Jarosław
%A Chmielewski, Leszek, J.
%A Górski, Jarosław
%A Orłowski, Arkadiusz
%Z Part 5: Industrial Management and Other Applications
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 16th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM)
%C Bialystok, Poland
%Y Khalid Saeed
%Y Władysław Homenda
%Y Rituparna Chaki
%I Springer International Publishing
%3 Computer Information Systems and Industrial Management
%V LNCS-10244
%P 402-411
%8 2017-06-16
%D 2017
%R 10.1007/978-3-319-59105-6_34
%K Orange skin
%K Surface defect
%K Detection
%K Quality inspection
%K Furniture
%K Textural features
%K Classification
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X 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.
%G English
%Z TC 8
%2 https://inria.hal.science/hal-01656207/document
%2 https://inria.hal.science/hal-01656207/file/448933_1_En_34_Chapter.pdf
%L hal-01656207
%U https://inria.hal.science/hal-01656207
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC8
%~ IFIP-CISIM
%~ IFIP-LNCS-10244