Automatic Classification of Wood Defects using Support Vector Machines
Paper i proceeding, 2008

This paper addresses the issue of automatic wood defect classification. We propose a tree-structure support vector machine (SVM) to classify four types of wood knots by using images captured from lumber boards. Simple and effective features are proposed and extracted by first partitioning the knot images into 3 distinct areas, followed by applying an 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 has resulted in an average classification rate of 96.5% and false alarm rate of 2.25% over 400 test knot images. Our future work includes more extensive tests on large data set and the extension of knot types.

wood knot classification

support vector machine

machine vision.

wood defect detection

wood defect inspection

feature extraction


Irene Yu-Hua Gu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Hans Andersson

Chalmers, Signaler och system

Raul Vicen

Universidad de Alcala

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 5337 356-367
3642023444 (ISBN)


Datavetenskap (datalogi)

Datorseende och robotik (autonoma system)





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