Self-organizing feature extraction in recognition of wood surface defects and color images

A method for constructing classification features with unsupervised learning is presented. The method is based on clustering of the high dimensional measurements into a small number of features with self-organizing maps. The histograms of the self-organized features are classified with a multilayer perceptron network, that can pick up the relevant features and feature combinations from the histograms. The method is applied in two industrial problems, color image recognition for selection of optimal reproduction parameters in the printing press, and defect classification in wood surfaces. In both applications, the results were evaluated by the domain experts to be sufficient with respect to the application requirements. In the color image recognition the results were compared to the manually selected parameter settings, and in 12% of the test images there were distinguishable differences, with very few clear failures. Performance of the wood defect classification system was evaluated with a set of 400 knot images with 7 classes from spruce boards. The recognition rate was about 85% with only gray level images, giving about 90% accuracy for the final board grading, to be compared to 75-80% accuracy that can be maintained by a human inspector. As the methods are based on rather generic basic features and learning of the final features and classes from the samples, they are easily adaptable to different tasks, such as defect inspection in lumber or veneer, with different tree species or different cutting processes.