Development of color-vision-based solutions for lumber grading

Hannu Kauppinen; Olli Silven

doi:10.1117/12.205507

27 March 1995 Development of color-vision-based solutions for lumber grading

Hannu Kauppinen, Olli Silven

Proceedings Volume 2423, Machine Vision Applications in Industrial Inspection III; (1995) https://doi.org/10.1117/12.205507
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1995, San Jose, CA, United States

Abstract

The accuracy of the visual inspection process that performs the quality classification of lumber is one of the key interest areas in the mechanical wood industry. In principle, the quality classification of wood is straightforward: the class of each board depends on its defects and their distribution, as defined by the quality standard. However, even the appearance of sound wood varies greatly and there are no two boards or defects that have exactly the same properties such as color and texture. We describe the development of a color vision technology for grading softwood lumber. Much attention has been given to the early and cheap recognition of sound wood regions, as only a minor portion of the surface area of boards, around 5 - 10%, is defective. The non-interesting regions can be discarded and the hardware and communication bandwidth requirements at later defect identification stages are relieved. In the end the description of the board and its defects is passed to a grader that searches for all the applicable quality classes from the given set of standards. Extensive comparative tests have been carried out in a complete simulated system. The effects of changes in the spectrum of illumination have been evaluated to identify robust color features and to produce the requirements for color calibration.

Citation Download Citation

Hannu Kauppinen and Olli Silven "Development of color-vision-based solutions for lumber grading", Proc. SPIE 2423, Machine Vision Applications in Industrial Inspection III, (27 March 1995); https://doi.org/10.1117/12.205507

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