Thermographic imaging for use in artificial intelligence and vision algorithms

The constant technological innovation in devices for the acquisition of digital images such as: energy-efficient and high-pixel sensors, memories with greater storage capacity and processors capable of sampling digital signals more quickly, have made it possible to digitize with greater reliability...

Full description

Autores:
Silva, Jesús
Echeverría, Ana María
Varela Izquierdo, Noel
Pineda, Omar
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7955
Acceso en línea:
https://hdl.handle.net/11323/7955
https://doi.org/10.1088/1757-899X/872/1/012035
https://repositorio.cuc.edu.co/
Palabra clave:
Visualization of the heat
Ventral
Zone of the animal
Thermogram
Rights
openAccess
License
CC0 1.0 Universal
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repository_id_str
dc.title.spa.fl_str_mv Thermographic imaging for use in artificial intelligence and vision algorithms
title Thermographic imaging for use in artificial intelligence and vision algorithms
spellingShingle Thermographic imaging for use in artificial intelligence and vision algorithms
Visualization of the heat
Ventral
Zone of the animal
Thermogram
title_short Thermographic imaging for use in artificial intelligence and vision algorithms
title_full Thermographic imaging for use in artificial intelligence and vision algorithms
title_fullStr Thermographic imaging for use in artificial intelligence and vision algorithms
title_full_unstemmed Thermographic imaging for use in artificial intelligence and vision algorithms
title_sort Thermographic imaging for use in artificial intelligence and vision algorithms
dc.creator.fl_str_mv Silva, Jesús
Echeverría, Ana María
Varela Izquierdo, Noel
Pineda, Omar
dc.contributor.author.spa.fl_str_mv Silva, Jesús
Echeverría, Ana María
Varela Izquierdo, Noel
Pineda, Omar
dc.subject.spa.fl_str_mv Visualization of the heat
Ventral
Zone of the animal
Thermogram
topic Visualization of the heat
Ventral
Zone of the animal
Thermogram
description The constant technological innovation in devices for the acquisition of digital images such as: energy-efficient and high-pixel sensors, memories with greater storage capacity and processors capable of sampling digital signals more quickly, have made it possible to digitize with greater reliability real life scenes in an instant of time, making it possible to analyze and interpret different physical phenomena [1][2][3] such as fractures in materials, evasion of obstacles, weather conditions, injury detection, among others, giving rise to a new line of research called Artificial Vision (AV) focused on generating algorithms to improve image quality, segment characteristics of interest and eventually recognize patterns, in order to make more efficient image processing for the solution of problems in robotics, automation, security, medicine, veterinary, and others. The research aims to develop a database of thermographic images of pregnant and non-pregnant sheep, providing a tool for specialists in the area of computer intelligence and artificial vision.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020-09-15
dc.date.accessioned.none.fl_str_mv 2021-03-03T19:22:43Z
dc.date.available.none.fl_str_mv 2021-03-03T19:22:43Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.issn.spa.fl_str_mv 17578981
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dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1088/1757-899X/872/1/012035
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
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Corporación Universidad de la Costa
REDICUC - Repositorio CUC
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https://doi.org/10.1088/1757-899X/872/1/012035
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv [1] Chernov V., Alander J. and Bochko V. 2015 Integer-based accurate conversion between RGB and HSV color spaces Computers & Electrical Engineering 46 328-337
[2] Metzner M., Sauter-Louis C., Seemueller A., Petzl W. and Zerbe H. 2015 Infrared thermography of the udder after experimentally induced Escherichia coli mastitis in cows The Veterinary Journal 204 360-362
[3] Systems FLIR AB. 2011 Guía de termografía para mantenimiento predictivo Guía informativa del uso de cámaras termográficas en aplicaciones industriales FLIR
[4] McManus C., Tanure C. B., Peripolli V., Seixas L., Fischer V., Gabbi A. M., Menegassi S. R., Stumpf M. T., Kolling G. J., Dias E. and Costa J. B. G. 2016 Infrared thermography in animal production: An overview Computers and Electronics in Agriculture 123 10-16
[5] Oliveira J. V. P., Coelho A. L. F., Silva L. C. C., Viana L. A., Pinto A. C. V., Pinto F. A. C. and Oliveira Filho D. 2020 Using image pre-mapping for applications of monitoring electrical switchboards Automation in Construction 112 103091
[6] Viloria A. and Gaitan-Angulo M. 2016 Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company Indian Journal Of Science And Technology 9
[7] Wei C., Liu Y., Bie Y., Wang S., Wu Y., Wang T. and Yin K. 2020 In Proceedings of PURPLE MOUNTAIN FORUM 2019-International Forum on Smart Grid Protection and Control (Singapore: Springer) The Fault Diagnosis of Infrared Bushing Images Based on Infrared Thermography 803-812
[8] Cárdenas Quiroga E. A., Morales Martin L. Y. and Ussa Caycedo A. 2015 La estereoscopia, métodos y aplicaciones en diferentes áreas del conocimiento Revista Científica. General José María Córdova, Escuela Militar de Cadetes General José María Córdova 13
[9] Yang R., Du B., Duan P., He Y., Wang H., He Y. and Zhang K. 2019 Electromagnetic Induction Heating and Image Fusion of Silicon Photovoltaic Cell Electro-Thermography and Electroluminescence IEEE Transactions on Industrial Informatics
[10] Rizkin B. A., Popovich K. and Hartman R. L. 2019 Artificial Neural Network control of thermoelectrically-cooled microfluidics using computer vision based on IR thermography Computers & Chemical Engineering 121 584-593
[11] Bhatia Y., Rai R., Gupta V., Aggarwal N. and Akula A. 2019 Convolutional neural networks-based potholes detection using thermal imaging Journal of King Saud University-Computer and Information Sciences
[12] Babao R. P., Bianzon F., Co M. L., Cruz M. D., Corales N. C., Flores J. D. and Baldelomar E. L. 2017 In 2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (IEEE) Integration of visual and thermographic images in an artificial neural network for object classification 1-5 December
[13] Ward S., Hensler J., Alsalam B. and Gonzalez L. F. 2016 In 2016 IEEE Aerospace Conference (IEEE) Autonomous UAVs wildlife detection using thermal imaging, predictive navigation and computer vision 1-8 March
[14] Byrne D. T., Berry D. P., Esmonde H., Govern Mc F., Creighton P. and McHugh N. 2019 Infrared thermography as a tool to detect hoof lesions in sheep Translational Animal Science 3 577-588
[15] Viloria A. and Gaitan-Angulo M. 2016 Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company Indian Journal Of Science And Technology 9
[16] Cannas S., Palestrini C., Canali E., Cozzi B., Ferri N., Heinzl E. and Dalla Costa E. 2018 Thermography as a Non-Invasive Measure of Stress and Fear of Humans in Sheep Animals 8 146
[17] Seixas L., Melo de C. B., Tanure C. B., Peripolli V. and McManus C. 2017 Heat tolerance in Brazilian hair sheep Asian-Australasian journal of animal sciences 30 593
[18] Seixas L., Melo de C. B., Menezes A. M., Ramos A. F., Paludo G. R., Peripolli V. and McManus C. 2017 Study on environmental indices and heat tolerance tests in hair sheep Tropical animal health and production 49 975-982
[19] Sanchez L., Vásquez C. and Viloria A. 2018 In International Conference on Data Mining and Big data (Cham: Springer) Conglomerates of Latin American countries and public policies for the sustainable development of the electric power generation sector 759-766 June
[20] Gowan Mc N. E., Scantlebury D. M., Cowan E., Burch K. J., Maule A. G. and Marks N. J. 2020 Dietary effects on pelage emissivity in mammals: Implications for infrared thermography Journal of Thermal Biology 102516
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spelling Silva, JesúsEcheverría, Ana MaríaVarela Izquierdo, NoelPineda, Omar2021-03-03T19:22:43Z2021-03-03T19:22:43Z2020-09-1517578981https://hdl.handle.net/11323/7955https://doi.org/10.1088/1757-899X/872/1/012035Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The constant technological innovation in devices for the acquisition of digital images such as: energy-efficient and high-pixel sensors, memories with greater storage capacity and processors capable of sampling digital signals more quickly, have made it possible to digitize with greater reliability real life scenes in an instant of time, making it possible to analyze and interpret different physical phenomena [1][2][3] such as fractures in materials, evasion of obstacles, weather conditions, injury detection, among others, giving rise to a new line of research called Artificial Vision (AV) focused on generating algorithms to improve image quality, segment characteristics of interest and eventually recognize patterns, in order to make more efficient image processing for the solution of problems in robotics, automation, security, medicine, veterinary, and others. The research aims to develop a database of thermographic images of pregnant and non-pregnant sheep, providing a tool for specialists in the area of computer intelligence and artificial vision.Silva, JesúsEcheverría, Ana MaríaVarela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600application/pdfengCorporación Universidad de la CostaRetractedCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2IOP Conf. Series: Materials Science and Engineeringhttps://iopscience.iop.org/article/10.1088/1757-899X/872/1/012035/pdfVisualization of the heatVentralZone of the animalThermogramThermographic imaging for use in artificial intelligence and vision algorithmsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Chernov V., Alander J. and Bochko V. 2015 Integer-based accurate conversion between RGB and HSV color spaces Computers & Electrical Engineering 46 328-337[2] Metzner M., Sauter-Louis C., Seemueller A., Petzl W. and Zerbe H. 2015 Infrared thermography of the udder after experimentally induced Escherichia coli mastitis in cows The Veterinary Journal 204 360-362[3] Systems FLIR AB. 2011 Guía de termografía para mantenimiento predictivo Guía informativa del uso de cámaras termográficas en aplicaciones industriales FLIR[4] McManus C., Tanure C. B., Peripolli V., Seixas L., Fischer V., Gabbi A. M., Menegassi S. R., Stumpf M. T., Kolling G. J., Dias E. and Costa J. B. G. 2016 Infrared thermography in animal production: An overview Computers and Electronics in Agriculture 123 10-16[5] Oliveira J. V. P., Coelho A. L. F., Silva L. C. C., Viana L. A., Pinto A. C. V., Pinto F. A. C. and Oliveira Filho D. 2020 Using image pre-mapping for applications of monitoring electrical switchboards Automation in Construction 112 103091[6] Viloria A. and Gaitan-Angulo M. 2016 Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company Indian Journal Of Science And Technology 9[7] Wei C., Liu Y., Bie Y., Wang S., Wu Y., Wang T. and Yin K. 2020 In Proceedings of PURPLE MOUNTAIN FORUM 2019-International Forum on Smart Grid Protection and Control (Singapore: Springer) The Fault Diagnosis of Infrared Bushing Images Based on Infrared Thermography 803-812[8] Cárdenas Quiroga E. A., Morales Martin L. Y. and Ussa Caycedo A. 2015 La estereoscopia, métodos y aplicaciones en diferentes áreas del conocimiento Revista Científica. General José María Córdova, Escuela Militar de Cadetes General José María Córdova 13[9] Yang R., Du B., Duan P., He Y., Wang H., He Y. and Zhang K. 2019 Electromagnetic Induction Heating and Image Fusion of Silicon Photovoltaic Cell Electro-Thermography and Electroluminescence IEEE Transactions on Industrial Informatics[10] Rizkin B. A., Popovich K. and Hartman R. L. 2019 Artificial Neural Network control of thermoelectrically-cooled microfluidics using computer vision based on IR thermography Computers & Chemical Engineering 121 584-593[11] Bhatia Y., Rai R., Gupta V., Aggarwal N. and Akula A. 2019 Convolutional neural networks-based potholes detection using thermal imaging Journal of King Saud University-Computer and Information Sciences[12] Babao R. P., Bianzon F., Co M. L., Cruz M. D., Corales N. C., Flores J. D. and Baldelomar E. L. 2017 In 2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (IEEE) Integration of visual and thermographic images in an artificial neural network for object classification 1-5 December[13] Ward S., Hensler J., Alsalam B. and Gonzalez L. F. 2016 In 2016 IEEE Aerospace Conference (IEEE) Autonomous UAVs wildlife detection using thermal imaging, predictive navigation and computer vision 1-8 March[14] Byrne D. T., Berry D. P., Esmonde H., Govern Mc F., Creighton P. and McHugh N. 2019 Infrared thermography as a tool to detect hoof lesions in sheep Translational Animal Science 3 577-588[15] Viloria A. and Gaitan-Angulo M. 2016 Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company Indian Journal Of Science And Technology 9[16] Cannas S., Palestrini C., Canali E., Cozzi B., Ferri N., Heinzl E. and Dalla Costa E. 2018 Thermography as a Non-Invasive Measure of Stress and Fear of Humans in Sheep Animals 8 146[17] Seixas L., Melo de C. B., Tanure C. B., Peripolli V. and McManus C. 2017 Heat tolerance in Brazilian hair sheep Asian-Australasian journal of animal sciences 30 593[18] Seixas L., Melo de C. B., Menezes A. M., Ramos A. F., Paludo G. R., Peripolli V. and McManus C. 2017 Study on environmental indices and heat tolerance tests in hair sheep Tropical animal health and production 49 975-982[19] Sanchez L., Vásquez C. and Viloria A. 2018 In International Conference on Data Mining and Big data (Cham: Springer) Conglomerates of Latin American countries and public policies for the sustainable development of the electric power generation sector 759-766 June[20] Gowan Mc N. E., Scantlebury D. M., Cowan E., Burch K. J., Maule A. G. and Marks N. 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