Hierarchical classification of surface defects on dusty wood boards

A hierarchical classification procedure is proposed to classify nine classes of red oak boards, eight classes of surface defects, and a class of clear wood. It utilizes prior knowledge about surface defects and texture information. After the preprocessing, the dark and bright regions on the board images are separated. The background and the wane defect are identified using their locations as prior knowledge. The rest of the surface defects and clear wood are divided into three subsets: circle, line, and texture. The surface defects belonging to each subset are characterized using the mean of the gray levels and the CAR model parameters. The approach improves the resolution of the defect locations. It also reduces the computational time as compared to approaches based only on the texture information. The feasibility of applying this procedure to the images of wood boards in a dusty environment was investigated. It was found that the performance of the proposed hierarchical procedure deteriorates when the sample boards are at least partly covered by dust