Wood Defect Classification based on Image Analysis and Support Vector Machine
Journal article, 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

SVM

Author

Irene Yu-Hua Gu

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Hans Andersson

Chalmers, Signals and Systems

Raul Vicen

University of Alcalá

Wood Science and Technology

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

Vol. 44 4 693-704

Subject Categories

Wood Science

Computer Science

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1007/s00226-009-0287-9

More information

Latest update

5/30/2018