Wood Defect Classification based on Image Analysis and Support Vector Machine
Artikel i vetenskaplig tidskrift, 2010

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.

wood defect classication

wood defect detection



Irene Yu-Hua Gu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Hans Andersson

Chalmers, Signaler och system

Raul Vicen

Universidad de Alcala

Wood Science and Technology

0043-7719 (ISSN) 1432-5225 (eISSN)

Vol. 44 4 693-704



Datavetenskap (datalogi)

Datorseende och robotik (autonoma system)



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