Mature pomegranate recognition methods in natural environments using machine vision

Detalhes bibliográficos
Autor(a) principal: Lei,Xiangxiao
Data de Publicação: 2019
Outros Autores: Ouyang,Honglin, Xu,Lijuan
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.
id UFSM-2_05c139d4ea0f170cc319d47276c18d79
oai_identifier_str oai:scielo:S0103-84782019000900351
network_acronym_str UFSM-2
network_name_str Ciência rural (Online)
repository_id_str
spelling 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

Registros relacionados