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]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/978-3-642-25085-9_63
http://hdl.handle.net/11449/72815
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. © 2011 Springer-Verlag.
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spelling A study on automatic methods based on mathematical morphology for Martian dust devil tracks detectionDust Devils TracksFeature DetectionMarsMathematical MorphologyAutomatic DetectionAutomatic methodData setsDust devilsFeature detectionGround truthMartian dustSurface of MarsTime of processingComputer visionDustStatistical testsSurface testingMathematical morphologyThis 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. © 2011 Springer-Verlag.Instituto Federal de Educação, Ciência e Tecnologia de Mato Grosso - IFMT, 95 Zulmira Canavarro, 780025-200, CuiabáCentro de Recursos Naturais e Ambiente Instituto Superior Técnico - IST, Av. Rovisco Pais, 1049-001, LisboaUniversidade Estadual Paulista Faculdade de Ciências e Tecnologia - FCT, 305 Roberto Simonsen, 19060-900, Presidente PrudenteUniversidade Estadual Paulista Faculdade de Ciências e Tecnologia - FCT, 305 Roberto Simonsen, 19060-900, Presidente PrudenteInstituto Federal de Educação, Ciência e Tecnologia de Mato Grosso - IFMTInstituto Superior Técnico - ISTUniversidade Estadual Paulista (Unesp)Statella, ThiagoPina, PedroSilva, Erivaldo Antonio da [UNESP]2014-05-27T11:26:11Z2014-05-27T11:26:11Z2011-11-28info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject533-540http://dx.doi.org/10.1007/978-3-642-25085-9_63Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 7042 LNCS, p. 533-540.0302-97431611-3349http://hdl.handle.net/11449/7281510.1007/978-3-642-25085-9_632-s2.0-8185517712791035450045071350000-0002-7069-0479Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)0,295info:eu-repo/semantics/openAccess2021-10-23T21:41:37Zoai:repositorio.unesp.br:11449/72815Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T21:41:37Repositório 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
Dust Devils Tracks
Feature Detection
Mars
Mathematical Morphology
Automatic Detection
Automatic method
Data sets
Dust devils
Feature detection
Ground truth
Martian dust
Surface of Mars
Time of processing
Computer vision
Dust
Statistical tests
Surface testing
Mathematical morphology
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]
author_role author
author2 Pina, Pedro
Silva, Erivaldo Antonio da [UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv Instituto Federal de Educação, Ciência e Tecnologia de Mato Grosso - IFMT
Instituto Superior Técnico - IST
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Statella, Thiago
Pina, Pedro
Silva, Erivaldo Antonio da [UNESP]
dc.subject.por.fl_str_mv Dust Devils Tracks
Feature Detection
Mars
Mathematical Morphology
Automatic Detection
Automatic method
Data sets
Dust devils
Feature detection
Ground truth
Martian dust
Surface of Mars
Time of processing
Computer vision
Dust
Statistical tests
Surface testing
Mathematical morphology
topic Dust Devils Tracks
Feature Detection
Mars
Mathematical Morphology
Automatic Detection
Automatic method
Data sets
Dust devils
Feature detection
Ground truth
Martian dust
Surface of Mars
Time of processing
Computer vision
Dust
Statistical tests
Surface testing
Mathematical morphology
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. © 2011 Springer-Verlag.
publishDate 2011
dc.date.none.fl_str_mv 2011-11-28
2014-05-27T11:26:11Z
2014-05-27T11:26:11Z
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 http://dx.doi.org/10.1007/978-3-642-25085-9_63
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 7042 LNCS, p. 533-540.
0302-9743
1611-3349
http://hdl.handle.net/11449/72815
10.1007/978-3-642-25085-9_63
2-s2.0-81855177127
9103545004507135
0000-0002-7069-0479
url http://dx.doi.org/10.1007/978-3-642-25085-9_63
http://hdl.handle.net/11449/72815
identifier_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 7042 LNCS, p. 533-540.
0302-9743
1611-3349
10.1007/978-3-642-25085-9_63
2-s2.0-81855177127
9103545004507135
0000-0002-7069-0479
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
0,295
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 533-540
dc.source.none.fl_str_mv Scopus
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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