A Study on Automatic Methods Based on Mathematical Morphology for Martian Dust Devil Tracks Detection

Detalhes bibliográficos
Autor(a) principal: Statella, Thiago
Data de Publicação: 2011
Outros Autores: Pina, Pedro, Silva, Erivaldo Antonio da [UNESP], Martin, C. S., Kim, S. W.
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/197413
Resumo: This paper presents three methods for automatic detection of dust devils tracks in images of Mars. The methods are mainly based on Mathematical Morphology and results of their performance are analyzed and compared. A dataset of 21 images from the surface of Mars representative of the diversity of those track features were considered for developing, testing and evaluating our methods, confronting their outputs with ground truth images made manually. Methods 1 and 3, based on closing top-hat and path closing top-hat, respectively, showed similar mean accuracies around 90% but the time of processing was much greater for method 1 than for method 3. Method 2, based on radial closing, was the fastest but showed worse mean accuracy. Thus, this was the tiebreak factor.
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spelling A Study on Automatic Methods Based on Mathematical Morphology for Martian Dust Devil Tracks DetectionMarsDust Devils TracksMathematical MorphologyFeature DetectionThis paper presents three methods for automatic detection of dust devils tracks in images of Mars. The methods are mainly based on Mathematical Morphology and results of their performance are analyzed and compared. A dataset of 21 images from the surface of Mars representative of the diversity of those track features were considered for developing, testing and evaluating our methods, confronting their outputs with ground truth images made manually. Methods 1 and 3, based on closing top-hat and path closing top-hat, respectively, showed similar mean accuracies around 90% but the time of processing was much greater for method 1 than for method 3. Method 2, based on radial closing, was the fastest but showed worse mean accuracy. Thus, this was the tiebreak factor.Inst Fed Educ Ciencia & Tecnol Mato Grosso IFMT, 95 Zulmira Canavarro, BR-78002520 Cuiaba, BrazilCtr Recursos Naturais Ambiente, Inst Super Tecn, P-1049001 Lisbon, PortugalUniv Estadual Paulista, Fac Ciencias & Tecnol, BR-19060900 Presidente Prudente, BrazilUniv Estadual Paulista, Fac Ciencias & Tecnol, BR-19060900 Presidente Prudente, BrazilSpringerInst Fed Educ Ciencia & Tecnol Mato Grosso IFMTCtr Recursos Naturais AmbienteUniversidade Estadual Paulista (Unesp)Statella, ThiagoPina, PedroSilva, Erivaldo Antonio da [UNESP]Martin, C. S.Kim, S. W.2020-12-10T22:02:36Z2020-12-10T22:02:36Z2011-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject533-+Progress In Pattern Recognition, Image Analysis, Computer Vision, And Applications. Berlin: Springer-verlag Berlin, v. 7042, p. 533-+, 2011.0302-9743http://hdl.handle.net/11449/197413WOS:000307257600063Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProgress In Pattern Recognition, Image Analysis, Computer Vision, And Applicationsinfo:eu-repo/semantics/openAccess2024-06-18T15:02:39Zoai:repositorio.unesp.br:11449/197413Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:24:05.221245Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv A Study on Automatic Methods Based on Mathematical Morphology for Martian Dust Devil Tracks Detection
title A Study on Automatic Methods Based on Mathematical Morphology for Martian Dust Devil Tracks Detection
spellingShingle A Study on Automatic Methods Based on Mathematical Morphology for Martian Dust Devil Tracks Detection
Statella, Thiago
Mars
Dust Devils Tracks
Mathematical Morphology
Feature Detection
title_short A Study on Automatic Methods Based on Mathematical Morphology for Martian Dust Devil Tracks Detection
title_full A Study on Automatic Methods Based on Mathematical Morphology for Martian Dust Devil Tracks Detection
title_fullStr A Study on Automatic Methods Based on Mathematical Morphology for Martian Dust Devil Tracks Detection
title_full_unstemmed A Study on Automatic Methods Based on Mathematical Morphology for Martian Dust Devil Tracks Detection
title_sort A Study on Automatic Methods Based on Mathematical Morphology for Martian Dust Devil Tracks Detection
author Statella, Thiago
author_facet Statella, Thiago
Pina, Pedro
Silva, Erivaldo Antonio da [UNESP]
Martin, C. S.
Kim, S. W.
author_role author
author2 Pina, Pedro
Silva, Erivaldo Antonio da [UNESP]
Martin, C. S.
Kim, S. W.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Inst Fed Educ Ciencia & Tecnol Mato Grosso IFMT
Ctr Recursos Naturais Ambiente
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Statella, Thiago
Pina, Pedro
Silva, Erivaldo Antonio da [UNESP]
Martin, C. S.
Kim, S. W.
dc.subject.por.fl_str_mv Mars
Dust Devils Tracks
Mathematical Morphology
Feature Detection
topic Mars
Dust Devils Tracks
Mathematical Morphology
Feature Detection
description This paper presents three methods for automatic detection of dust devils tracks in images of Mars. The methods are mainly based on Mathematical Morphology and results of their performance are analyzed and compared. A dataset of 21 images from the surface of Mars representative of the diversity of those track features were considered for developing, testing and evaluating our methods, confronting their outputs with ground truth images made manually. Methods 1 and 3, based on closing top-hat and path closing top-hat, respectively, showed similar mean accuracies around 90% but the time of processing was much greater for method 1 than for method 3. Method 2, based on radial closing, was the fastest but showed worse mean accuracy. Thus, this was the tiebreak factor.
publishDate 2011
dc.date.none.fl_str_mv 2011-01-01
2020-12-10T22:02:36Z
2020-12-10T22:02:36Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv Progress In Pattern Recognition, Image Analysis, Computer Vision, And Applications. Berlin: Springer-verlag Berlin, v. 7042, p. 533-+, 2011.
0302-9743
http://hdl.handle.net/11449/197413
WOS:000307257600063
identifier_str_mv Progress In Pattern Recognition, Image Analysis, Computer Vision, And Applications. Berlin: Springer-verlag Berlin, v. 7042, p. 533-+, 2011.
0302-9743
WOS:000307257600063
url http://hdl.handle.net/11449/197413
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Progress In Pattern Recognition, Image Analysis, Computer Vision, And Applications
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 533-+
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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