Rotational Invariant Wood Species Recognition through Wood Species Verification

An automated wood species recognition system using computer vision techniques is not widely used today, it is highly needed in various industries, but a wood identification expert is not easily trained to meet the market demand. This paper proposes a rotational invariant method using the grey level co-occurrence matrices (GLCM) as the features, an energy value representing the similarity between the test sample and the template is computed to decide whether the test sample is the same species as the template. A template is accepted when the energy is lower than the threshold value. The species with the highest number of accepted templates will be regarded as the recognition result. The experiment is conducted on six wood species of the CAIRO dataset with a total of 450 training samples and 60 testing samples and achieved a result of 80.00%.