Analysis of different parameters of influence in industrial cameras calibration processes

Industrial vision highlights a growing trend in industrial systems. As camera sensors become smarter, the quality of data produced increases and it improves the accuracy results. One of the most decisive steps for getting accurate measurements is the calibration process. This paper aims to analyze t...

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Autores: Moru, D.K. (Desmond Kehinde)|||/items/88b8daba-5490-46bf-8c73-aac98829f21a, Borro-Yagüez, D. (Diego)|||/items/aa8f720b-c296-4606-9d41-0e83058a79b5
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad de Navarra
Repositorio:Dadun. Depósito Académico Digital de la Universidad de Navarra
Idioma:inglés
OAI Identifier:oai:dadun.unav.edu:10171/119568
Acceso en línea:https://hdl.handle.net/10171/119568
Access Level:acceso abierto
Palabra clave:Calibration
Machine vision
Camera focus
Calibration error
Multivariable analysis
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spelling Analysis of different parameters of influence in industrial cameras calibration processesMoru, D.K. (Desmond Kehinde)|||/items/88b8daba-5490-46bf-8c73-aac98829f21aBorro-Yagüez, D. (Diego)|||/items/aa8f720b-c296-4606-9d41-0e83058a79b5CalibrationMachine visionCamera focusCalibration errorMultivariable analysisIndustrial vision highlights a growing trend in industrial systems. As camera sensors become smarter, the quality of data produced increases and it improves the accuracy results. One of the most decisive steps for getting accurate measurements is the calibration process. This paper aims to analyze the effect of four calibration parameters: camera focus, exposure time, calibration plate tilt and number of images, on the calibration accuracy. Endocentric and telecentric lenses are used in the image acquisition and a comparative quality analysis of the calibration result is obtained using statistical methods. A sample of 2176 images is used to generate the population and the calibration error is obtained for the different values of the parameters of interest. To study the influence of each parameter in the calibration error, a multivariable statistical analysis is performed. Statistically significant results were obtained for all parameters, except in the exposure time parameter, leading to the conclusion that the calibration results (and hence the measurement accuracy) can be improved by choosing the appropriate calibration parameters.Dadun. Depósito Académico Digital Universidad de Navarra20212021-01-0120212021-01-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10171/119568reponame:Dadun. Depósito Académico Digital de la Universidad de Navarrainstname:Universidad de NavarraInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:dadun.unav.edu:10171/1195682026-06-21T12:47:57Z
dc.title.none.fl_str_mv Analysis of different parameters of influence in industrial cameras calibration processes
title Analysis of different parameters of influence in industrial cameras calibration processes
spellingShingle Analysis of different parameters of influence in industrial cameras calibration processes
Moru, D.K. (Desmond Kehinde)|||/items/88b8daba-5490-46bf-8c73-aac98829f21a
Calibration
Machine vision
Camera focus
Calibration error
Multivariable analysis
title_short Analysis of different parameters of influence in industrial cameras calibration processes
title_full Analysis of different parameters of influence in industrial cameras calibration processes
title_fullStr Analysis of different parameters of influence in industrial cameras calibration processes
title_full_unstemmed Analysis of different parameters of influence in industrial cameras calibration processes
title_sort Analysis of different parameters of influence in industrial cameras calibration processes
dc.creator.none.fl_str_mv Moru, D.K. (Desmond Kehinde)|||/items/88b8daba-5490-46bf-8c73-aac98829f21a
Borro-Yagüez, D. (Diego)|||/items/aa8f720b-c296-4606-9d41-0e83058a79b5
author Moru, D.K. (Desmond Kehinde)|||/items/88b8daba-5490-46bf-8c73-aac98829f21a
author_facet Moru, D.K. (Desmond Kehinde)|||/items/88b8daba-5490-46bf-8c73-aac98829f21a
Borro-Yagüez, D. (Diego)|||/items/aa8f720b-c296-4606-9d41-0e83058a79b5
author_role author
author2 Borro-Yagüez, D. (Diego)|||/items/aa8f720b-c296-4606-9d41-0e83058a79b5
author2_role author
dc.contributor.none.fl_str_mv Dadun. Depósito Académico Digital Universidad de Navarra
dc.subject.none.fl_str_mv Calibration
Machine vision
Camera focus
Calibration error
Multivariable analysis
topic Calibration
Machine vision
Camera focus
Calibration error
Multivariable analysis
description Industrial vision highlights a growing trend in industrial systems. As camera sensors become smarter, the quality of data produced increases and it improves the accuracy results. One of the most decisive steps for getting accurate measurements is the calibration process. This paper aims to analyze the effect of four calibration parameters: camera focus, exposure time, calibration plate tilt and number of images, on the calibration accuracy. Endocentric and telecentric lenses are used in the image acquisition and a comparative quality analysis of the calibration result is obtained using statistical methods. A sample of 2176 images is used to generate the population and the calibration error is obtained for the different values of the parameters of interest. To study the influence of each parameter in the calibration error, a multivariable statistical analysis is performed. Statistically significant results were obtained for all parameters, except in the exposure time parameter, leading to the conclusion that the calibration results (and hence the measurement accuracy) can be improved by choosing the appropriate calibration parameters.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-01-01
2021
2021-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10171/119568
url https://hdl.handle.net/10171/119568
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dadun. Depósito Académico Digital de la Universidad de Navarra
instname:Universidad de Navarra
instname_str Universidad de Navarra
reponame_str Dadun. Depósito Académico Digital de la Universidad de Navarra
collection Dadun. Depósito Académico Digital de la Universidad de Navarra
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,198674

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