Abstract:

[Objective] Knot is an important evaluation index in the classification of wood veneer. The quantity of veneer knots and the maximum knot area can, to some extent, determine the grade of a wood veneer. Whereas by now, the classification of wood veneer processed in China mainly depends on visual inspection, which is of low efficiency. Therefore, quick identification and area assessment are performed to the surface knot of wood veneer with image recognition. Instead of artificial sorting is automatic classification by computer smart control, which can significantly promote the classification efficiency of wood veneer and is of great significance for the development and progress of wood industry. [Method] The wood veneer with knots are selected as object in this study. Bases on the preliminary results of image identification, an improved identification calculation for wood veneer knots using mathematical morphology is proposed. In order to solve the problem of missing characteristic quantity of partial knots or identification of non-knot characteristic quantity existing in the image identification of wood veneer, this work can be divided into 5 steps, those were, original image extraction, graying processing, image segmentation, margin inspection of characteristic quantity and knot identification. Firstly, images of wood veneer are collected, and grey level transformation is performed for the images for sequential image identification. Secondly, according to the knots in the gray images and different gray scope in the background, the image is split with the gray threshold chosen by the maximum entropy principle, so as to preliminarily separate the knots from the background. Then the interference characteristics outside the knots preliminarily selected are removed with morphological algorithm, thus the outer contour of knots can be accurately presented. Finally, outline assessment is performed for the characteristics detected, to prevent other factors such as crack and dirt being separated from the background due to their dark color and considered as knots. [Result] This study shows that, there are some interference characteristics around the knots after image segmentation, the relationship between interference characteristics and knots can be cut off by morphological expansion, and the corrosion operation after expansion can maintain the real size of knots. By comparing the morphological opening-and-closing operations, it is found that the knots processed by morphological closing operation can be more easily identified. The identification accuracy can be improved by performing ellipse fitting and outline condition restriction for the characteristic profile inspected, to prevent the identification of non-knots. Furthermore, knots can be preliminary assessed by calculating the characteristic profile points and the matching degree of ellipse, and the knots outline restriction is mainly used for filtering the influence of rectangular objects (such as crack) that can be fitted into ellipse. [Conclusion] The knots quantity and relative size on the surface of wood veneer can be obtained by visual inspection, in the practical production processes, after interfacing with hardware, the real size of knots can be obtained according to the relative position of image collecting equipment and collecting objects and the resolution of images collected, etc. by combining the system assessment results, thus to realize the automatic classification of wood veneer.

Key words: wood veneer, knot, image recognition, image segmentation, mathematical morphology, automatic classification