Colour classification of rubberwood boards for fingerjoint manufacturing using a SOM neural network and image processing

In order to produce a high quality rubberwood fingerjoint with highly uniform colour, wood boards of naturally different shades and colours are required to be elaborately classified and grouped. Within each group, wood boards of comparable shade and colour are then cut and joined to form a highly uniform shade and colour fingerjoint of the required dimensions. Currently, many manufacturers in Thailand still rely heavily on a manual classification process by an expert. In this paper, an automatic approach based on a combination of an image processing technique and an artificial neural network is presented. The Kohonen self organizing map (SOM) is selected and used for training with modified histogram data from the hue colour component of the rubberwood boards’ images. The outcome SOM is then used to classify an unknown colour rubberwood board with a novel colour group identification algorithm. The overall approach has proved effective in classifying the unknown colour of boards with as high as 95% accuracy without human intervention. In many cases, the approach provides invaluable information to guide an operator to easily classify the remaining 5%.