Abstract
Automated grading of lumber is attractive for the sawmill industry. Achieving better accuracy of quality grading and control of quality variation in volume production easily improves the profit obtained from it. We describe a color vision based framework to grading softwood lumber. The proposed inspection principle is to recognize the sound wood regions early, as this reduces the computational requirements at later defect recognition stages. The recognition stages are ordered based on their estimated costs and implementational complexity. This is important because color increases the data volumes significantly over grey scale images. Finally, the description of the board and its defects is passed to the quality grader which assigns the class giving the best price for each board. In comparative tests, the computational solutions have turned out to be simpler than with grey level images, compensating for the higher cost of the color imaging system. The effects of changes in the spectrum of illumination have also been evaluated to identify robust color features and to produce the requirements for color calibration.
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