Machine vision for timber grading singularities detection and applications

Mohamad Mazen Hittawe; Désiré Sidibé; Ouadi Beya; Fabrice Mériaudeau

doi:10.1117/1.JEI.26.6.063015

14 November 2017 Machine vision for timber grading singularities detection and applications

Mohamad Mazen Hittawe, Désiré Sidibé, Ouadi Beya, Fabrice Mériaudeau

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 6, 063015 (November 2017). https://doi.org/10.1117/1.JEI.26.6.063015

Abstract

This article deals with machine vision techniques applied to timber grading singularities. Timber used for architectural purposes must satisfy certain mechanical requirements, and, therefore, must be mechanically graded to ensure the manufacturer that the product complies with the requirements. However, the timber material has many singularities, such as knots, cracks, and presence of juvenile wood, which influence its mechanical behavior. Thus, identifying those singularities is of great importance. We address the problem of timber defects segmentation and classification and propose a method to detect timber defects such as cracks and knots using a bag-of-words approach. Extensive experimental results show that the proposed methods are efficient and can improve grading machines performances. We also propose an automated method for the detection of transverse knots, which allows the computation of knot depth ratio (KDR) images. Finally, we propose a method for the detection of juvenile wood regions based on tree rings detection and the estimation of the tree’s pith. The experimental results show that the proposed methods achieve excellent results for knots detection, with a recall of 0.94 and 0.95 on two datasets, as well as for KDR image computation and juvenile timber detection.

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Mohamad Mazen Hittawe, Désiré Sidibé, Ouadi Beya, and Fabrice Mériaudeau "Machine vision for timber grading singularities detection and applications," Journal of Electronic Imaging 26(6), 063015 (14 November 2017). https://doi.org/10.1117/1.JEI.26.6.063015

Received: 4 April 2017; Accepted: 26 October 2017; Published: 14 November 2017

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