Abstract
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.