Detecção automática de sombras em imagens aéreas e terrestres
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1590/s1982-21702017000400038 http://hdl.handle.net/11449/175618 |
Resumo: | Shadows exist in almost all aerial and outdoor images, and they can be useful for estimating Sun position estimation or measuring object size. On the other hand, they represent a problem in processes such as object detection/recognition, image matching, etc., because they may be confused with dark objects and change the image radiometric properties. We address this problem on aerial and outdoor color images in this work. We use a filter to find low intensities as a first step. For outdoor color images, we analyze spectrum ratio properties to refine the detection, and the results are assessed with a dataset containing ground truth. For the aerial case we validate the detections depending of the hue component of pixels. This stage takes into account that, in deep shadows, most pixels have blue or violet wavelengths because of an atmospheric scattering effect. |
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Repositório Institucional da UNESP |
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Detecção automática de sombras em imagens aéreas e terrestresAutomatic shadow detection in aerial and terrestrial imagesAerial ImagesShadow DetectionTerrestrial ImagesShadows exist in almost all aerial and outdoor images, and they can be useful for estimating Sun position estimation or measuring object size. On the other hand, they represent a problem in processes such as object detection/recognition, image matching, etc., because they may be confused with dark objects and change the image radiometric properties. We address this problem on aerial and outdoor color images in this work. We use a filter to find low intensities as a first step. For outdoor color images, we analyze spectrum ratio properties to refine the detection, and the results are assessed with a dataset containing ground truth. For the aerial case we validate the detections depending of the hue component of pixels. This stage takes into account that, in deep shadows, most pixels have blue or violet wavelengths because of an atmospheric scattering effect.Instituto Nacional de Pesquisas Espaciais - INPEUniversidade Estadual Paulista Júlio de Mesquita Filho – UNESP Departamento de CartografiaUniversidade Estadual Paulista Júlio de Mesquita Filho – UNESP Departamento de CartografiaInstituto Nacional de Pesquisas Espaciais - INPEUniversidade Estadual Paulista (Unesp)Freitas, Vander Luis de SouzaReis, Barbara Maximino da FonsecaTommaselli, Antonio Maria Garcia [UNESP]2018-12-11T17:16:44Z2018-12-11T17:16:44Z2017-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article578-590application/pdfhttp://dx.doi.org/10.1590/s1982-21702017000400038Boletim de Ciencias Geodesicas, v. 23, n. 4, p. 578-590, 2017.1982-21701413-4853http://hdl.handle.net/11449/17561810.1590/s1982-21702017000400038S1982-217020170004005782-s2.0-85037704989S1982-21702017000400578.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPporBoletim de Ciencias Geodesicas0,188info:eu-repo/semantics/openAccess2024-06-18T15:01:53Zoai:repositorio.unesp.br:11449/175618Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T22:04:45.868743Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Detecção automática de sombras em imagens aéreas e terrestres Automatic shadow detection in aerial and terrestrial images |
title |
Detecção automática de sombras em imagens aéreas e terrestres |
spellingShingle |
Detecção automática de sombras em imagens aéreas e terrestres Freitas, Vander Luis de Souza Aerial Images Shadow Detection Terrestrial Images |
title_short |
Detecção automática de sombras em imagens aéreas e terrestres |
title_full |
Detecção automática de sombras em imagens aéreas e terrestres |
title_fullStr |
Detecção automática de sombras em imagens aéreas e terrestres |
title_full_unstemmed |
Detecção automática de sombras em imagens aéreas e terrestres |
title_sort |
Detecção automática de sombras em imagens aéreas e terrestres |
author |
Freitas, Vander Luis de Souza |
author_facet |
Freitas, Vander Luis de Souza Reis, Barbara Maximino da Fonseca Tommaselli, Antonio Maria Garcia [UNESP] |
author_role |
author |
author2 |
Reis, Barbara Maximino da Fonseca Tommaselli, Antonio Maria Garcia [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Instituto Nacional de Pesquisas Espaciais - INPE Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Freitas, Vander Luis de Souza Reis, Barbara Maximino da Fonseca Tommaselli, Antonio Maria Garcia [UNESP] |
dc.subject.por.fl_str_mv |
Aerial Images Shadow Detection Terrestrial Images |
topic |
Aerial Images Shadow Detection Terrestrial Images |
description |
Shadows exist in almost all aerial and outdoor images, and they can be useful for estimating Sun position estimation or measuring object size. On the other hand, they represent a problem in processes such as object detection/recognition, image matching, etc., because they may be confused with dark objects and change the image radiometric properties. We address this problem on aerial and outdoor color images in this work. We use a filter to find low intensities as a first step. For outdoor color images, we analyze spectrum ratio properties to refine the detection, and the results are assessed with a dataset containing ground truth. For the aerial case we validate the detections depending of the hue component of pixels. This stage takes into account that, in deep shadows, most pixels have blue or violet wavelengths because of an atmospheric scattering effect. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-01 2018-12-11T17:16:44Z 2018-12-11T17:16:44Z |
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://dx.doi.org/10.1590/s1982-21702017000400038 Boletim de Ciencias Geodesicas, v. 23, n. 4, p. 578-590, 2017. 1982-2170 1413-4853 http://hdl.handle.net/11449/175618 10.1590/s1982-21702017000400038 S1982-21702017000400578 2-s2.0-85037704989 S1982-21702017000400578.pdf |
url |
http://dx.doi.org/10.1590/s1982-21702017000400038 http://hdl.handle.net/11449/175618 |
identifier_str_mv |
Boletim de Ciencias Geodesicas, v. 23, n. 4, p. 578-590, 2017. 1982-2170 1413-4853 10.1590/s1982-21702017000400038 S1982-21702017000400578 2-s2.0-85037704989 S1982-21702017000400578.pdf |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
Boletim de Ciencias Geodesicas 0,188 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
578-590 application/pdf |
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 |
|
_version_ |
1808129390236663808 |