Computer vision system approach in colour measurements of foods: Part I. development of methodology
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Food Science and Technology (Campinas) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612016000200382 |
Resumo: | Abstract The colour assessment ability of the computer vision system is investigated and the data are compared with colour measurements taken by a conventional colorimeter. Linear and quadratic models are built to improve currently used methodology for the conversion of RGB colour units to L * a * b * colour space. For this purpose, two innovative ideas are proposed and tested. First, substantial amount of colour tones is generated to cover as many points in the colour space as possible. Secondly, the colour space is calibrated separately, whereas in previous research in the literature, the colour space is calibrated simultaneously. It is found that the RGB colour units to L * a * b * colour space transformation approach proposed in this study is more logical and more accurate, and the prediction performance of the quadratic model is superior over the linear model. |
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Food Science and Technology (Campinas) |
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Computer vision system approach in colour measurements of foods: Part I. development of methodologycolourcomputer vision systemcolorimeterRGBL* a* b*Abstract The colour assessment ability of the computer vision system is investigated and the data are compared with colour measurements taken by a conventional colorimeter. Linear and quadratic models are built to improve currently used methodology for the conversion of RGB colour units to L * a * b * colour space. For this purpose, two innovative ideas are proposed and tested. First, substantial amount of colour tones is generated to cover as many points in the colour space as possible. Secondly, the colour space is calibrated separately, whereas in previous research in the literature, the colour space is calibrated simultaneously. It is found that the RGB colour units to L * a * b * colour space transformation approach proposed in this study is more logical and more accurate, and the prediction performance of the quadratic model is superior over the linear model.Sociedade Brasileira de Ciência e Tecnologia de Alimentos2016-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612016000200382Food Science and Technology v.36 n.2 2016reponame:Food Science and Technology (Campinas)instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)instacron:SBCTA10.1590/1678-457X.11615info:eu-repo/semantics/openAccessTARLAK,FatihOZDEMİR,MuratMELİKOGLU,Mehmeteng2016-07-08T00:00:00Zoai:scielo:S0101-20612016000200382Revistahttp://www.scielo.br/ctaONGhttps://old.scielo.br/oai/scielo-oai.php||revista@sbcta.org.br1678-457X0101-2061opendoar:2016-07-08T00:00Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)false |
dc.title.none.fl_str_mv |
Computer vision system approach in colour measurements of foods: Part I. development of methodology |
title |
Computer vision system approach in colour measurements of foods: Part I. development of methodology |
spellingShingle |
Computer vision system approach in colour measurements of foods: Part I. development of methodology TARLAK,Fatih colour computer vision system colorimeter RGB L* a* b* |
title_short |
Computer vision system approach in colour measurements of foods: Part I. development of methodology |
title_full |
Computer vision system approach in colour measurements of foods: Part I. development of methodology |
title_fullStr |
Computer vision system approach in colour measurements of foods: Part I. development of methodology |
title_full_unstemmed |
Computer vision system approach in colour measurements of foods: Part I. development of methodology |
title_sort |
Computer vision system approach in colour measurements of foods: Part I. development of methodology |
author |
TARLAK,Fatih |
author_facet |
TARLAK,Fatih OZDEMİR,Murat MELİKOGLU,Mehmet |
author_role |
author |
author2 |
OZDEMİR,Murat MELİKOGLU,Mehmet |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
TARLAK,Fatih OZDEMİR,Murat MELİKOGLU,Mehmet |
dc.subject.por.fl_str_mv |
colour computer vision system colorimeter RGB L* a* b* |
topic |
colour computer vision system colorimeter RGB L* a* b* |
description |
Abstract The colour assessment ability of the computer vision system is investigated and the data are compared with colour measurements taken by a conventional colorimeter. Linear and quadratic models are built to improve currently used methodology for the conversion of RGB colour units to L * a * b * colour space. For this purpose, two innovative ideas are proposed and tested. First, substantial amount of colour tones is generated to cover as many points in the colour space as possible. Secondly, the colour space is calibrated separately, whereas in previous research in the literature, the colour space is calibrated simultaneously. It is found that the RGB colour units to L * a * b * colour space transformation approach proposed in this study is more logical and more accurate, and the prediction performance of the quadratic model is superior over the linear model. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-06-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612016000200382 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612016000200382 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1678-457X.11615 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Ciência e Tecnologia de Alimentos |
publisher.none.fl_str_mv |
Sociedade Brasileira de Ciência e Tecnologia de Alimentos |
dc.source.none.fl_str_mv |
Food Science and Technology v.36 n.2 2016 reponame:Food Science and Technology (Campinas) instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) instacron:SBCTA |
instname_str |
Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) |
instacron_str |
SBCTA |
institution |
SBCTA |
reponame_str |
Food Science and Technology (Campinas) |
collection |
Food Science and Technology (Campinas) |
repository.name.fl_str_mv |
Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) |
repository.mail.fl_str_mv |
||revista@sbcta.org.br |
_version_ |
1752126320767139840 |