A validation strategy for a target-based vision tracking system with an industrial robot
Computer vision tracking systems are used in many medical and industrial applications. Understanding and modeling the tracking errors for a given system aids in the correct implementation and operation for optimal measurement results. This project aims to simulate and experimentally validate a track...
- Autores:
-
Romero, C
Naufal, C
Meza, J
Marrugo Hernández, Andrés Guillermo
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9382
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9382
https://iopscience.iop.org/article/10.1088/1742-6596/1547/1/012018
- Palabra clave:
- Industrial robots
Medical imaging
Target tracking
Error assessment
Experimental verification
Optimal measurements
Performance assessment
Target position
Tracking system
Validation strategies
Vision tracking systems
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc/4.0/
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| dc.title.spa.fl_str_mv |
A validation strategy for a target-based vision tracking system with an industrial robot |
| title |
A validation strategy for a target-based vision tracking system with an industrial robot |
| spellingShingle |
A validation strategy for a target-based vision tracking system with an industrial robot Industrial robots Medical imaging Target tracking Error assessment Experimental verification Optimal measurements Performance assessment Target position Tracking system Validation strategies Vision tracking systems |
| title_short |
A validation strategy for a target-based vision tracking system with an industrial robot |
| title_full |
A validation strategy for a target-based vision tracking system with an industrial robot |
| title_fullStr |
A validation strategy for a target-based vision tracking system with an industrial robot |
| title_full_unstemmed |
A validation strategy for a target-based vision tracking system with an industrial robot |
| title_sort |
A validation strategy for a target-based vision tracking system with an industrial robot |
| dc.creator.fl_str_mv |
Romero, C Naufal, C Meza, J Marrugo Hernández, Andrés Guillermo |
| dc.contributor.author.none.fl_str_mv |
Romero, C Naufal, C Meza, J Marrugo Hernández, Andrés Guillermo |
| dc.subject.keywords.spa.fl_str_mv |
Industrial robots Medical imaging Target tracking Error assessment Experimental verification Optimal measurements Performance assessment Target position Tracking system Validation strategies Vision tracking systems |
| topic |
Industrial robots Medical imaging Target tracking Error assessment Experimental verification Optimal measurements Performance assessment Target position Tracking system Validation strategies Vision tracking systems |
| description |
Computer vision tracking systems are used in many medical and industrial applications. Understanding and modeling the tracking errors for a given system aids in the correct implementation and operation for optimal measurement results. This project aims to simulate and experimentally validate a tracking system for medical imaging. In this work, we developed a validation strategy for a target-based vision tracking system with an industrial robot. The simulation results show that the system can be accurately modeled, and the error assessment strategy is robust. Experimental verification with an EPSON C3 robot shows the reliability of the vision tracking system to obtain the target position and pose accurately. The general-purpose performance assessment strategy can be used as a vision tracking evaluation mechanism to ensure the system performance is adequate for a given application. |
| publishDate |
2020 |
| dc.date.accessioned.none.fl_str_mv |
2020-09-10T21:23:41Z |
| dc.date.available.none.fl_str_mv |
2020-09-10T21:23:41Z |
| dc.date.issued.none.fl_str_mv |
2020-05-20 |
| dc.date.submitted.none.fl_str_mv |
2020-09-07 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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info:eu-repo/semantics/lecture |
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info:eu-repo/semantics/publishedVersion |
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Otro |
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publishedVersion |
| dc.identifier.citation.spa.fl_str_mv |
Romero, C., Naufal, C., Meza, J., & Marrugo, A. G. (2020). A validation strategy for a target-based vision tracking system with an industrial robot. Paper presented at the Journal of Physics: Conference Series, , 1547(1) doi:10.1088/1742-6596/1547/1/012018 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/9382 |
| dc.identifier.url.none.fl_str_mv |
https://iopscience.iop.org/article/10.1088/1742-6596/1547/1/012018 |
| dc.identifier.doi.none.fl_str_mv |
10.1088/1742-6596/1547/1/012018 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Tecnológica de Bolívar |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Romero, C., Naufal, C., Meza, J., & Marrugo, A. G. (2020). A validation strategy for a target-based vision tracking system with an industrial robot. Paper presented at the Journal of Physics: Conference Series, , 1547(1) doi:10.1088/1742-6596/1547/1/012018 10.1088/1742-6596/1547/1/012018 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/9382 https://iopscience.iop.org/article/10.1088/1742-6596/1547/1/012018 |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
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http://purl.org/coar/access_right/c_abf2 |
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http://creativecommons.org/licenses/by-nc/4.0/ |
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info:eu-repo/semantics/openAccess |
| dc.rights.cc.*.fl_str_mv |
Atribución-NoComercial 4.0 Internacional |
| rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc/4.0/ Atribución-NoComercial 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
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8 páginas |
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application/pdf |
| dc.publisher.place.spa.fl_str_mv |
Cartagena de Indias |
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Journal of Physics: Conference Series, Volume 1547, Issue 1, article id. 012018 (2020). |
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Universidad Tecnológica de Bolívar |
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Romero, Cb548c497-18d2-443f-beb3-a700d55d2617Naufal, C671d5773-e3f0-434f-a036-18d37445c40cMeza, J123e2815-9357-4495-878a-c226558ac63dMarrugo Hernández, Andrés Guillermoc8c00abe-7817-4172-bc7e-f4758f7b12cb2020-09-10T21:23:41Z2020-09-10T21:23:41Z2020-05-202020-09-07Romero, C., Naufal, C., Meza, J., & Marrugo, A. G. (2020). A validation strategy for a target-based vision tracking system with an industrial robot. Paper presented at the Journal of Physics: Conference Series, , 1547(1) doi:10.1088/1742-6596/1547/1/012018https://hdl.handle.net/20.500.12585/9382https://iopscience.iop.org/article/10.1088/1742-6596/1547/1/01201810.1088/1742-6596/1547/1/012018Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarComputer vision tracking systems are used in many medical and industrial applications. Understanding and modeling the tracking errors for a given system aids in the correct implementation and operation for optimal measurement results. This project aims to simulate and experimentally validate a tracking system for medical imaging. In this work, we developed a validation strategy for a target-based vision tracking system with an industrial robot. The simulation results show that the system can be accurately modeled, and the error assessment strategy is robust. Experimental verification with an EPSON C3 robot shows the reliability of the vision tracking system to obtain the target position and pose accurately. The general-purpose performance assessment strategy can be used as a vision tracking evaluation mechanism to ensure the system performance is adequate for a given application.8 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Journal of Physics: Conference Series, Volume 1547, Issue 1, article id. 012018 (2020).A validation strategy for a target-based vision tracking system with an industrial robotinfo:eu-repo/semantics/lectureinfo:eu-repo/semantics/publishedVersionOtrohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_8544Industrial robotsMedical imagingTarget trackingError assessmentExperimental verificationOptimal measurementsPerformance assessmentTarget positionTracking systemValidation strategiesVision tracking systemsCartagena de IndiasInvestigadoresVoulodimos A, Doulamis N, Doulamis A and Protopapadakis E 2018 Computational Intelligence and Neuroscience 2018(7068349)de Oliveira Baldner F, Costa P B, Gomes J F S and Leta F R 2020 A review on computer vision applied to mechanical tests in search for better accuracy Advances in Visualization and Optimization Techniques for Multidisciplinary Research (Singapore: Springer) pp 265–281Janai J, G¨uney F, Behl A and Geiger A 2019 ArXiv abs/1704.05519v2Mozaffari M H and Lee W S 2017 Ultrasound in Medicine and Biology 43(10)Ferber R, Osis S T, Hicks J L and Delp S L 2016 Journal of Biomechanics 49(16) 3759Cenni F, Monari D, Desloovere K, Aertbeli¨en E, Schless S H and Bruyninckx H 2016 Computer Methods and Programs in Biomedicine 136(C) 179Bouget D, Allan M, Stoyanov D and Jannin P 2017 Medical Image Analysis 35 633Yang J, Cheng T, Teizer J, Vela P A and Shi Z 2011 Advanced Engineering Informatics 25(4) 736Meza J, Simarra P, Contreras-Ojeda S, Romero L A, Contreras-Ortiz S H, Arambula Cosio F and Marrugo A G 2019 A low-cost multi-modal medical imaging system with fringe projection profilometry and 3d freehand ultrasound 15th International Symposium on Medical Information Processing and Analysis vol 11330 (Colombia: International Society for Optics and Photonics)El-Gohary M and McNames J 2015 IEEE Transactions on Biomedical Engineering 62(7) 1759Hartley R and Zisserman A 2003 Multiple View Geometry in Computer Vision (United States of America: Cambridge University Press)Zhang Z 2000 IEEE Transactions on Pattern Analysis and Machine IntelligenceCraig J J 2009 Introduction to Robotics: Mechanics and Control, 3/E (India: Pearson Education)Corke P 2007 IEEE Robotics & Automation Magazine 14(4) 16Corke P I 2005 IEEE Robotics & Automation Magazine 12(4) 16http://purl.org/coar/resource_type/c_c94fORIGINAL31.pdf31.pdfPonenciaapplication/pdf1637217https://repositorio.utb.edu.co/bitstream/20.500.12585/9382/1/31.pdfaf7318e9cd516f77c5ba92ae4b19fe6eMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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