Type of Document	Master's Thesis
Author	He, Jing
Author's Email Address	hej@ctd.comsat.com
URN	etd-3198-94046
Title	A Comparison of Artificial Neural Network Classifiers for Analysis of CT Images for the Inspection of Hardwood Logs
Degree	Master of Science
Department	Electrical Engineering
Advisory Committee	Advisor Name Title Abbott, A. Lynn Committee Chair Schmoldt, Daniel L. Committee Member VanLandingham, Hugh F. Committee Member
Keywords	hardwood artificial neural network CT image
Date of Defense	1997-09-15
Availability	unrestricted
Abstract This thesis describes an automatic CT image interpretation approach that can be used to detect hardwood defects. The goal of this research has been to develop several automatic image interpretation systems for different types of wood, with lower-level processing performed by feed forward artificial neural networks. In the course of this work, five single-species classifiers and seven multiple-species classifiers have been developed for 2-D and 3-D analysis. These classifiers were trained with back-propagation, using training samples of three species of hardwood: cherry, red oak and yellow poplar. These classifiers recognize six classes: heartwood (clear wood), sapwood, knots, bark, split s and decay. This demonstrates the feasibility of developing general classifiers that can be used with different types of hardwood logs. This will help sawmill and veneer mill operators to improve the quality of products and preserve natural resources.
Files	Filename       Size       Approximate Download Time (Hours:Minutes:Seconds)     28.8 Modem   56K Modem   ISDN (64 Kb)   ISDN (128 Kb)   Higher-speed Access    part1.pdf 15.88 Kb 00:00:04 00:00:02 00:00:01 < 00:00:01 < 00:00:01   part2.pdf 675.30 Kb 00:03:07 00:01:36 00:01:24 00:00:42 00:00:03

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