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Proceedings Paper

Computer Vision System For Locating And Identifying Defects In Hardwood Lumber
Author(s): Richard W. Conners; Chong T. Ng; Tai-Hoon Cho; Charles W. McMillin
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Paper Abstract

This paper describes research aimed at developing an automatic cutup system for use in the rough mills of the hardwood furniture and fixture industry. In particular, this paper describes attempts to create the vision system that will power this automatic cutup system. There are a number of factors that make the development of such a vision system a challenge. First there is the innate variability of the wood material itself. No two species look exactly the same, in fact, they can have a significant visual difference in appearance among species. Yet a truly robust vision system must be able to handle a variety of such species, preferably with no operator intervention required when changing from one species to another. Secondly, there is a good deal of variability in the definition of what constitutes a removable defect. The hardwood furniture and fixture industry is diverse in the nature of the products that it makes. The products range from hardwood flooring to fancy hardwood furniture, from simple mill work to kitchen cabinets. Thus depending on the manufacturer, the product, and the quality of the product the nature of what constitutes a removable defect can and does vary. The vision system must be such that it can be tailored to meet each of these unique needs, preferably without any additional program modifications. This paper will describe the vision system that has been developed. It will assess the current system capabilities, and it will discuss the directions for future research. It will be argued that artificial intelligence methods provide a natural mechanism for attacking this computer vision application.

Paper Details

Date Published: 21 March 1989
PDF: 18 pages
Proc. SPIE 1095, Applications of Artificial Intelligence VII, (21 March 1989); doi: 10.1117/12.969258
Show Author Affiliations
Richard W. Conners, Virginia Polytechnic Institute and State University (United States)
Chong T. Ng, Virginia Polytechnic Institute and State University (United States)
Tai-Hoon Cho, Virginia Polytechnic Institute and State University (United States)
Charles W. McMillin, Southern Forest Experiment Station (United States)


Published in SPIE Proceedings Vol. 1095:
Applications of Artificial Intelligence VII
Mohan M. Trivedi, Editor(s)

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