Title page for ETD etd-3198-94046


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.

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