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Wood Defect Classification based on Image Analysis and Support Vector Machine

Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling) ; Henrik Andersson (Institutionen för signaler och system) ; Raul Vicen
Wood Science and Technology (0043-7719). Vol. 44 (2010), 4, p. 693-704.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: wood defect detection, wood defect classication, SVM

Denna post skapades 2009-11-27. Senast ändrad 2017-08-18.
CPL Pubid: 102364


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Institutioner (Chalmers)

Institutionen för signaler och system, Signalbehandling (1900-2017)
Institutionen för signaler och system (1900-2017)


Datavetenskap (datalogi)

Chalmers infrastruktur