This thesis describes the design of an image interpretation system for the automatic
detection of internal hardwood log defects. The goal of the research is that such a system
should not only be able to identify and locate internal defects of hardwood logs using
computed tomography (CT) imagery, but also should be able to accommodate more than
one type of wood, and should show potential for real-time industrial implementation. This
thesis describes a new image classification system that utilizes a feed forward artificial
neural network as the image classifier. The classifier was trained with back-propagation,
using training samples collected from two different types of hardwood logs, red oak and
water oak. Pre-processing and post-processing are perfonned to increase the system
classification perfonnance, and to make the system be able to accommodate more than one
wood type. It is shown in this thesis that such a neural-net based approach can yield a high
classification accuracy, and it shows a high potential for parallelism. Several possible
design alternatives and comparisons are also addressed in the thesis. The final image
interpretation system has been successfully tested, exhibiting a classification accuracy of
95% with test images from four hardwood logs.
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