Detection of Surface Defects of Type ‘orange skin’ in Furniture Elements with Conventional Image Processing Methods

Chmielewski, Leszek J.; Orłowski, Arkadiusz; Śmietańska, Katarzyna; Górski, J.; Krajewski, Krzysztof; Janowicz, Maciej; Wilkowski, Jacek; Kietlińska, Krystyna

doi:10.1007/978-3-319-30285-0_3

Cited by 4 publications

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“…Similar problem has been investigated in our previous publications [2,3]. Results obtained in those works suggested that the choice of features was not crucial to the classification result.…”

Section: Discussionsupporting

confidence: 53%

“…It occurred that the problem of the surface defect called orange skin requires special attention due to that the dimensional variability introduced by this defect is minute. In [2] we found that this defect seems to be easily detectable with pure vision techniques and in [3] we stated that by using typical off-theshelf textural features this defect is detectable in the majority of cases. In this paper we try to use some more advanced features already used by us in other, requiring applications [4,5,9].…”

Section: Introductionmentioning

confidence: 74%

“…Therefore, we did not pay much attention to this choice. However, the problem we investigated here was different from that described in [2], where as the classified object the whole product was considered, and there were only two classes: good and bad object. Now, we consider a small fragment of the product as the object to be classified.…”

Section: Discussionmentioning

confidence: 99%

“…After positive experience with the detection of the orange skin defect treated with simple methods in [2] and some more advanced, but still well known ones in [3], now we shall assess the possibilities and limits of the application of relatively complex textural features from the groups of Kolmogorow-Smirnow features [9], maximum subregion-based features [4], features using the concept of percolation [4] and on the Hilbert curve [5].…”

Section: Introductionmentioning

confidence: 99%

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

Kruk

Świderski

Śmietańska

et al. 2017

Computer Information Systems and Industrial Management

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Part 5: Industrial Management and Other ApplicationsInternational audienceThe 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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Section: Discussionsupporting

confidence: 53%