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.
Wood singularities detection is a primary step in wood grading enhancement. Our approach is purely machine vision based. The main objective is to compute physical properties like density, modulus of elasticity (MOE) and modulus of rupture (MOR) given wood surface images. Knots are one of the main singularities which directly affect the wood strength. Hence, our target is to detect knots and classify them into transverse and non-transverse ones. Then the Knots Depth Ratio (KDR) is computed based on all found transverse knots. Afterwards, KDR is used for the wood mechanical model improvement. Our technique is based on colour image analysis where the knots are detected by means of contrast intensity transformation and morphological operations. Then KDR computations are based on transverse knots and clear wood densities. Finally, MOE and MOR are computed using KDR images. The accuracy of number of knots found, their locations, MOE and MOR has been validated using a dataset of 252 images. In our dataset, these values were manually calculated. To the best of our knowledge our approach is the first purely machine vision based method to compute KDR, MOE and MOR.