Mature pomegranate recognition methods in natural environments using machine vision
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Ciência Rural |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782019000900351 |
Resumo: | ABSTRACT: The use of machine vision to recognize mature pomegranates in natural environments is of major significance in improving the applicability and work efficiency of picking robots. By analyzing the color characteristics of color images of mature pomegranates under different illumination conditions, the feasibility of the YCbCr color model for pomegranate image recognition under different illumination conditions was proven. First, the Cr component map of pomegranate image is selected and then the pomegranate fruit is segmented by the kernel fuzzy C-means clustering algorithm to obtain the pomegranate image. Contrast experiments of pomegranate image segmentation under different illumination conditions were then performed using the proposed kernel fuzzy C-means clustering algorithm, the fuzzy C-means clustering algorithm, the Otsu algorithm and the threshold segmentation algorithm. Results of the experiments verified the effectiveness and superiority of the proposed algorithm. |
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oai:scielo:S0103-84782019000900351 |
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UFSM-2 |
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Ciência rural (Online) |
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Mature pomegranate recognition methods in natural environments using machine visionPunica granatum L.machine visionfuzzy clusteringkernelimage segmentation.ABSTRACT: The use of machine vision to recognize mature pomegranates in natural environments is of major significance in improving the applicability and work efficiency of picking robots. By analyzing the color characteristics of color images of mature pomegranates under different illumination conditions, the feasibility of the YCbCr color model for pomegranate image recognition under different illumination conditions was proven. First, the Cr component map of pomegranate image is selected and then the pomegranate fruit is segmented by the kernel fuzzy C-means clustering algorithm to obtain the pomegranate image. Contrast experiments of pomegranate image segmentation under different illumination conditions were then performed using the proposed kernel fuzzy C-means clustering algorithm, the fuzzy C-means clustering algorithm, the Otsu algorithm and the threshold segmentation algorithm. Results of the experiments verified the effectiveness and superiority of the proposed algorithm.Universidade Federal de Santa Maria2019-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782019000900351Ciência Rural v.49 n.9 2019reponame:Ciência Ruralinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM10.1590/0103-8478cr20190298info:eu-repo/semantics/openAccessLei,XiangxiaoOuyang,HonglinXu,Lijuaneng2019-08-30T00:00:00ZRevista |
dc.title.none.fl_str_mv |
Mature pomegranate recognition methods in natural environments using machine vision |
title |
Mature pomegranate recognition methods in natural environments using machine vision |
spellingShingle |
Mature pomegranate recognition methods in natural environments using machine vision Lei,Xiangxiao Punica granatum L. machine vision fuzzy clustering kernel image segmentation. |
title_short |
Mature pomegranate recognition methods in natural environments using machine vision |
title_full |
Mature pomegranate recognition methods in natural environments using machine vision |
title_fullStr |
Mature pomegranate recognition methods in natural environments using machine vision |
title_full_unstemmed |
Mature pomegranate recognition methods in natural environments using machine vision |
title_sort |
Mature pomegranate recognition methods in natural environments using machine vision |
author |
Lei,Xiangxiao |
author_facet |
Lei,Xiangxiao Ouyang,Honglin Xu,Lijuan |
author_role |
author |
author2 |
Ouyang,Honglin Xu,Lijuan |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Lei,Xiangxiao Ouyang,Honglin Xu,Lijuan |
dc.subject.por.fl_str_mv |
Punica granatum L. machine vision fuzzy clustering kernel image segmentation. |
topic |
Punica granatum L. machine vision fuzzy clustering kernel image segmentation. |
description |
ABSTRACT: The use of machine vision to recognize mature pomegranates in natural environments is of major significance in improving the applicability and work efficiency of picking robots. By analyzing the color characteristics of color images of mature pomegranates under different illumination conditions, the feasibility of the YCbCr color model for pomegranate image recognition under different illumination conditions was proven. First, the Cr component map of pomegranate image is selected and then the pomegranate fruit is segmented by the kernel fuzzy C-means clustering algorithm to obtain the pomegranate image. Contrast experiments of pomegranate image segmentation under different illumination conditions were then performed using the proposed kernel fuzzy C-means clustering algorithm, the fuzzy C-means clustering algorithm, the Otsu algorithm and the threshold segmentation algorithm. Results of the experiments verified the effectiveness and superiority of the proposed algorithm. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01 |
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=S0103-84782019000900351 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782019000900351 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0103-8478cr20190298 |
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 |
Universidade Federal de Santa Maria |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria |
dc.source.none.fl_str_mv |
Ciência Rural v.49 n.9 2019 reponame:Ciência Rural instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
instname_str |
Universidade Federal de Santa Maria (UFSM) |
instacron_str |
UFSM |
institution |
UFSM |
reponame_str |
Ciência Rural |
collection |
Ciência Rural |
repository.name.fl_str_mv |
|
repository.mail.fl_str_mv |
|
_version_ |
1749140553955540992 |