Artificial intelligence based techniques for processing segmented images of wood boards

Abstract

Veneer boards are made by bonding together thin sheets of wood. Normally, grading of these sheets is carried out to ensure that only high-quality sheets are used to make high-quality boards. Frequently, this quality control task is performed by a human inspector. However, due to the speed and repetitive nature of the job, human graders cannot always grade the boards accurately. To improve the efficiency of grading, attempts are being made to automate it using automated visual inspection (AVI). Integral to the AVI process is segmentation, which is concerned with separating clear wood and defective areas in the image. The defective areas are labelled as segmented objects. However, after segmentation has been performed, two problems can occur. Firstly, clear wood areas may be falsely detected as defects and, secondly, a defect may be represented by more than one segmented object. This paper describes two techniques that have been used to overcome these problems. The techniques were inspired by the artificial intelligence (AI) techniques of fuzzy logic and self-organizing neural networks.