The Development of Classificational system of the decorational wood block using Machine Vision and Neural Network
碩士 === 國立屏東技術學院 === 機械工程技術研究所 === 85 === The present thesis was undertaken to establish color-grading system for decorational wood blocks by using machine vision technique associated withneural network classifier. The various color coordinate system inclu...
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ndltd-TW-085NPUST4880012015-10-13T18:05:28Z http://ndltd.ncl.edu.tw/handle/98412478357447104194 The Development of Classificational system of the decorational wood block using Machine Vision and Neural Network 應用機械視覺與類神經網路於裝磺木板色澤分類系統之研究 LEE, Chin Mao 李進茂 碩士 國立屏東技術學院 機械工程技術研究所 85 The present thesis was undertaken to establish color-grading system for decorational wood blocks by using machine vision technique associated withneural network classifier. The various color coordinate system including GRB''HIS '' XYZ '' YIQ and Yxy characterized as the input parameters of a error back- propagation neural network were respectively explored to simulate the results of the manual classification. Of all various color representation systems encountered here, the RGB componet system is most superior in the convergent performance. A four-layer error back-propagation neural networkemploying the GRB color describing factor as the input parameters was foundto has 81.2% grading accuracy in comparision with manual grading. Finally , thepropotype of color grading system comprised of machine vision, neural networkclassifier and grading mechanism were integrated by man/machine interface to solve the problems of being much laborious and not effective in this wood industry. lin Yi-Hong 林宜弘 1996 學位論文 ; thesis 92 zh-TW |
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碩士 === 國立屏東技術學院 === 機械工程技術研究所 === 85 === The present thesis was undertaken to establish color-grading
system for decorational wood blocks by using machine vision
technique associated withneural network classifier. The various
color coordinate system including GRB''HIS '' XYZ '' YIQ and Yxy
characterized as the input parameters of a error back-
propagation neural network were respectively explored to
simulate the results of the manual classification. Of all
various color representation systems encountered here, the RGB
componet system is most superior in the convergent performance.
A four-layer error back-propagation neural networkemploying the
GRB color describing factor as the input parameters was foundto
has 81.2% grading accuracy in comparision with manual grading.
Finally , thepropotype of color grading system comprised of
machine vision, neural networkclassifier and grading mechanism
were integrated by man/machine interface to solve the problems
of being much laborious and not effective in this wood industry.
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author2 |
lin Yi-Hong |
author_facet |
lin Yi-Hong LEE, Chin Mao 李進茂 |
author |
LEE, Chin Mao 李進茂 |
spellingShingle |
LEE, Chin Mao 李進茂 The Development of Classificational system of the decorational wood block using Machine Vision and Neural Network |
author_sort |
LEE, Chin Mao |
title |
The Development of Classificational system of the decorational wood block using Machine Vision and Neural Network |
title_short |
The Development of Classificational system of the decorational wood block using Machine Vision and Neural Network |
title_full |
The Development of Classificational system of the decorational wood block using Machine Vision and Neural Network |
title_fullStr |
The Development of Classificational system of the decorational wood block using Machine Vision and Neural Network |
title_full_unstemmed |
The Development of Classificational system of the decorational wood block using Machine Vision and Neural Network |
title_sort |
development of classificational system of the decorational wood block using machine vision and neural network |
publishDate |
1996 |
url |
http://ndltd.ncl.edu.tw/handle/98412478357447104194 |
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