Automatic classification of ornamental stones using Machine Learning techniques - a study applied to limestone.
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10174/34094 https://doi.org/10.23919/CISTI49556.2020.9140872 |
Resumo: | The industry of extraction and transformation of rock minerals has an enormous importance in the Portuguese trade balance. The export volume increases every year, and to maintain these results it is necessary to invest in the modernization and optimization of production processes, as well as, in the classification of raw materials. This study aims to implement a classification model of ornamental rocks through the analysis and classification of images, using machine learning algorithms. The recognition of the type of stone, through the capture of images and subsequent algorithmic analysis, will allow to define quality control scales in future processes, taking into account the different types of stone. In addition, it will also allow to develop models capable of helping in reducing the amount of raw material wasted. This work presents the steps taken to create a classification model, using a dataset of 2260 images distributed over four classes, three of which are very similar to color level and one with a different tone. In this study, the results of the application of three automatic classification algorithms are analyzed. In addition, a discussion of how types of images can improve results and the execution times of algorithms are presented. |
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Automatic classification of ornamental stones using Machine Learning techniques - a study applied to limestone.ornamental rocksmachine learningimageclassificationThe industry of extraction and transformation of rock minerals has an enormous importance in the Portuguese trade balance. The export volume increases every year, and to maintain these results it is necessary to invest in the modernization and optimization of production processes, as well as, in the classification of raw materials. This study aims to implement a classification model of ornamental rocks through the analysis and classification of images, using machine learning algorithms. The recognition of the type of stone, through the capture of images and subsequent algorithmic analysis, will allow to define quality control scales in future processes, taking into account the different types of stone. In addition, it will also allow to develop models capable of helping in reducing the amount of raw material wasted. This work presents the steps taken to create a classification model, using a dataset of 2260 images distributed over four classes, three of which are very similar to color level and one with a different tone. In this study, the results of the application of three automatic classification algorithms are analyzed. In addition, a discussion of how types of images can improve results and the execution times of algorithms are presented.IEEE2023-02-10T11:24:25Z2023-02-102020-06-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/34094https://doi.org/10.23919/CISTI49556.2020.9140872http://hdl.handle.net/10174/34094https://doi.org/10.23919/CISTI49556.2020.9140872porM. Tereso, L. Rato and T. Gonçalves, "Automatic classification of ornamental stones using Machine Learning techniques A study applied to limestone," 2020 15th Iberian Conference on Information Systems and Technologies (CISTI), Seville, Spain, 2020, pp. 1-6, doi: 10.23919/CISTI49556.2020.9140872.CIMA, CHRCndlmr@uevora.pttcg@uevora.pt283Tereso, MarcoRato, LuisGonçalves, Teresainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-03T19:36:31Zoai:dspace.uevora.pt:10174/34094Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:22:48.869592Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Automatic classification of ornamental stones using Machine Learning techniques - a study applied to limestone. |
title |
Automatic classification of ornamental stones using Machine Learning techniques - a study applied to limestone. |
spellingShingle |
Automatic classification of ornamental stones using Machine Learning techniques - a study applied to limestone. Tereso, Marco ornamental rocks machine learning image classification |
title_short |
Automatic classification of ornamental stones using Machine Learning techniques - a study applied to limestone. |
title_full |
Automatic classification of ornamental stones using Machine Learning techniques - a study applied to limestone. |
title_fullStr |
Automatic classification of ornamental stones using Machine Learning techniques - a study applied to limestone. |
title_full_unstemmed |
Automatic classification of ornamental stones using Machine Learning techniques - a study applied to limestone. |
title_sort |
Automatic classification of ornamental stones using Machine Learning techniques - a study applied to limestone. |
author |
Tereso, Marco |
author_facet |
Tereso, Marco Rato, Luis Gonçalves, Teresa |
author_role |
author |
author2 |
Rato, Luis Gonçalves, Teresa |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Tereso, Marco Rato, Luis Gonçalves, Teresa |
dc.subject.por.fl_str_mv |
ornamental rocks machine learning image classification |
topic |
ornamental rocks machine learning image classification |
description |
The industry of extraction and transformation of rock minerals has an enormous importance in the Portuguese trade balance. The export volume increases every year, and to maintain these results it is necessary to invest in the modernization and optimization of production processes, as well as, in the classification of raw materials. This study aims to implement a classification model of ornamental rocks through the analysis and classification of images, using machine learning algorithms. The recognition of the type of stone, through the capture of images and subsequent algorithmic analysis, will allow to define quality control scales in future processes, taking into account the different types of stone. In addition, it will also allow to develop models capable of helping in reducing the amount of raw material wasted. This work presents the steps taken to create a classification model, using a dataset of 2260 images distributed over four classes, three of which are very similar to color level and one with a different tone. In this study, the results of the application of three automatic classification algorithms are analyzed. In addition, a discussion of how types of images can improve results and the execution times of algorithms are presented. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-06-01T00:00:00Z 2023-02-10T11:24:25Z 2023-02-10 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10174/34094 https://doi.org/10.23919/CISTI49556.2020.9140872 http://hdl.handle.net/10174/34094 https://doi.org/10.23919/CISTI49556.2020.9140872 |
url |
http://hdl.handle.net/10174/34094 https://doi.org/10.23919/CISTI49556.2020.9140872 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
M. Tereso, L. Rato and T. Gonçalves, "Automatic classification of ornamental stones using Machine Learning techniques A study applied to limestone," 2020 15th Iberian Conference on Information Systems and Technologies (CISTI), Seville, Spain, 2020, pp. 1-6, doi: 10.23919/CISTI49556.2020.9140872. CIMA, CHRC nd lmr@uevora.pt tcg@uevora.pt 283 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
IEEE |
publisher.none.fl_str_mv |
IEEE |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799136710150324224 |