Identifying and Locating Surface Defects in Wood: Part of an Automated Lumber Processing System

Authors:
Richard W. Conners;Charles W. Mcmillin;Kingyao Lin;Ramon E. Vasquez-Espinosa
Affiliations:
MEMBER, IEEE, Department of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803.;Southern Forest Experiment Station, USDA Forest Service, 2500 Shreveport Highway, Pineville, LA 71360.;MEMBER, IEEE, Department of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803/ Bell Laboratories, Murray Hill, NJ 07974.;STUDENT MEMBER, IEEE, Department of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803.
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1983

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Abstract

Continued increases in the cost of materials and labor make it imperative for furniture manufacturers to control costs by improved yield and increased productivity. This paper describes an Automated Lumber Processing System (ALPS) that employs computer tomography, optical scanning technology, the calculation of an optimum cutting strategy, and a computer-driven laser cutting device. While certain major hardware components of ALPS are already commercially available, a major missing element is the automatic inspection system needed to locate and identify surface defects on boards. This paper reports research aimed at developing such an inspection system. The basic strategy is to divide the digital image of a board into a number of disjoint rectangular regions and classify each independently. This simple procedure has the advantage of allowing an obvious parallel processing implementation. The study shows that measures of tonal and pattern related qualities are needed. The tonal measures are the mean, variance, skewness, and kurtosis of the gray levels. The pattern related measures are those based on cooccurrence matrices. In this initial feasibility study, these combined measures yielded an overall 88.3 percent correct classification on the eight defects most commonly found in lumber. To minimize the number of calculations needed to make the required classifications a sequential classifier is proposed.