A featured-based strategy for stereovision matching in sensors with fish-eye lenses for forest environments

This paper describes a novel feature-based stereovision matching process based on a pair of omnidirectional images in forest stands acquired with a stereovision sensor equipped with fish-eye lenses. The stereo analysis problem consists of the following steps image acquisition, camera modelling, feat...

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Detalles Bibliográficos
Autores: Herrera, P. J., Pajares, Gonzalo, Guijarro Guzmán, Mercedes, Ruz, J. J., De la Cruz, Jesús Manuel, Montes, Fernando
Tipo de recurso: artículo
Fecha de publicación:2009
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/292227
Acceso en línea:http://hdl.handle.net/10261/292227
Access Level:acceso abierto
Palabra clave:Stereovision matching
Fish-eye lenses
Forest image segmentation
Feature based
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repository_id_str
spelling A featured-based strategy for stereovision matching in sensors with fish-eye lenses for forest environmentsHerrera, P. J.Pajares, GonzaloGuijarro Guzmán, MercedesRuz, J. J.De la Cruz, Jesús ManuelMontes, FernandoStereovision matchingFish-eye lensesForest image segmentationFeature basedThis paper describes a novel feature-based stereovision matching process based on a pair of omnidirectional images in forest stands acquired with a stereovision sensor equipped with fish-eye lenses. The stereo analysis problem consists of the following steps image acquisition, camera modelling, feature extraction, image matching and depth determination. Once the depths of significant points on the trees are obtained, the growing stock volume can be estimated by considering the geometrical camera modelling, which is the final goal. The key steps are feature extraction and image matching. This paper is devoted solely to these two steps. At a first stage a segmentation process extracts the trunks, which are the regions used as features, where each feature is identified through a set of attributes of properties useful for matching. In the second step the features are matched based on the application of the following four well known matching constraints, epipolar, similarity, ordering and uniqueness. The combination of the segmentation and matching processes for this specific kind of sensors make the main contribution of the paper. The method is tested with satisfactory results and compared against the human expert criterion. © 2009 by the authors.Peer reviewedMultidisciplinary Digital Publishing InstituteConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202320232009info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://hdl.handle.net/10261/292227reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésInstituto de Ciencias Forestales (ICIFOR)Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2922272026-05-22T06:33:51Z
dc.title.none.fl_str_mv A featured-based strategy for stereovision matching in sensors with fish-eye lenses for forest environments
title A featured-based strategy for stereovision matching in sensors with fish-eye lenses for forest environments
spellingShingle A featured-based strategy for stereovision matching in sensors with fish-eye lenses for forest environments
Herrera, P. J.
Stereovision matching
Fish-eye lenses
Forest image segmentation
Feature based
title_short A featured-based strategy for stereovision matching in sensors with fish-eye lenses for forest environments
title_full A featured-based strategy for stereovision matching in sensors with fish-eye lenses for forest environments
title_fullStr A featured-based strategy for stereovision matching in sensors with fish-eye lenses for forest environments
title_full_unstemmed A featured-based strategy for stereovision matching in sensors with fish-eye lenses for forest environments
title_sort A featured-based strategy for stereovision matching in sensors with fish-eye lenses for forest environments
dc.creator.none.fl_str_mv Herrera, P. J.
Pajares, Gonzalo
Guijarro Guzmán, Mercedes
Ruz, J. J.
De la Cruz, Jesús Manuel
Montes, Fernando
author Herrera, P. J.
author_facet Herrera, P. J.
Pajares, Gonzalo
Guijarro Guzmán, Mercedes
Ruz, J. J.
De la Cruz, Jesús Manuel
Montes, Fernando
author_role author
author2 Pajares, Gonzalo
Guijarro Guzmán, Mercedes
Ruz, J. J.
De la Cruz, Jesús Manuel
Montes, Fernando
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Stereovision matching
Fish-eye lenses
Forest image segmentation
Feature based
topic Stereovision matching
Fish-eye lenses
Forest image segmentation
Feature based
description This paper describes a novel feature-based stereovision matching process based on a pair of omnidirectional images in forest stands acquired with a stereovision sensor equipped with fish-eye lenses. The stereo analysis problem consists of the following steps image acquisition, camera modelling, feature extraction, image matching and depth determination. Once the depths of significant points on the trees are obtained, the growing stock volume can be estimated by considering the geometrical camera modelling, which is the final goal. The key steps are feature extraction and image matching. This paper is devoted solely to these two steps. At a first stage a segmentation process extracts the trunks, which are the regions used as features, where each feature is identified through a set of attributes of properties useful for matching. In the second step the features are matched based on the application of the following four well known matching constraints, epipolar, similarity, ordering and uniqueness. The combination of the segmentation and matching processes for this specific kind of sensors make the main contribution of the paper. The method is tested with satisfactory results and compared against the human expert criterion. © 2009 by the authors.
publishDate 2009
dc.date.none.fl_str_mv 2009
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/292227
url http://hdl.handle.net/10261/292227
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Instituto de Ciencias Forestales (ICIFOR)
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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