A Study on Automatic Methods Based on Mathematical Morphology for Martian Dust Devil Tracks Detection
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , , |
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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Repositório Institucional da UNESP |
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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 |
|
_version_ |
1808129063200489472 |