Wood defect classification based on image analysis and support vector machines |
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Authors: | Irene Yu-Hua Gu Henrik Andersson Raul Vicen |
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Institution: | 1. Department of Signals and Systems, Chalmers University of Technology, 41296, G?teborg, Sweden 2. Department of Signal Theory and Communications, University of Alcala, Alcala de Henares, Spain
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Abstract: | This paper addresses the issue of automatic wood defect classification. A tree-structure support vector machine (SVM) is proposed
to classify four types of wood knots by using images captured from lumber boards. Simple and effective features are proposed
and extracted by partitioning the knot images into three distinct areas, followed by utilizing a novel order statistic filter
to yield an average pseudo color feature in each area. Excellent results have been obtained for the proposed SVM classifier
that is trained by 800 wood knot images. Performance evaluation has shown that the proposed SVM classifier resulted in an
average classification rate of 96.5% and false alarm rate of 2.25% over 400 test knot images. Future work will include more
extensive tests on large data set and the extension of knot types. |
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