Computer Vision-based Wood Recognition System

Wood recognition is widely used in various areas and environments, e.g. construction industry, manufacturing, and ecology. The job is still being carried out mostly by wood identification experts until the present. Current implementations for wood recognition on computer systems are mainly desktop-based. Furthermore, they are not implemented using artificial intelligence techniques due to the variance between samples for a same species that could cause difficulties for obtaining accurate results. The paper studies the experimental results of the computer vision-based wood recognition using the Grey-level Co-occurrence Matrices (GLCM) and Multi-layer Perceptron (MLP) technique. The GLCMs are generated to obtain five features: contrast, correlation, energy, entropy, and homogeneity, from the wood images. The two experiments give recognition rates of 72% and 60% respectively on 25 test images on five different wood species. The results show that the GLCM can extract similar features for the same species regardless of the orientation of the input image, and allows the MLP to classify the species of wood accurately.