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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Main Authors: LEE, Chin Mao, 李進茂
Other Authors: lin Yi-Hong
Format: Others
Language:zh-TW
Published: 1996
Online Access:http://ndltd.ncl.edu.tw/handle/98412478357447104194
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spelling 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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description 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.
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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