Nondestructive rule-based defect detection and identification system in CT images of hardwood logs

Abstract

This paper is concerned with the detection of internal defects in hardwood logs. Because the commercial value of hardwood lumber is directly related to the quantity, type, and location of defects in the wood, sawing strategies are typically chosen in an attempt to minimize the defects in the resulting boards. Traditionally, the sawyer makes sawing decisions by visually examining the exterior of log and then revising the sawing strategy as more and more of the log’s interior is exposed. Significantly better results would be expected if internal defects were known, so that a globally optimum solution could be selected in advance. This paper addresses this problem through the analysis of computed tomography (CT) images of logs, for the purpose of detecting important hardwood defects. In particular, the paper considers defect-dependent postprocessing methods, based on mathematical morphology, that show promising results.