Growth signatures of rosette plants from time-lapse video

Plant growth is a dynamic process, and the precise course of events during early plant development is of major interest for plant research. In this work, we investigate the growth of rosette plants by processing time-lapse videos of growing plants, where we use Nicotiana tabacum (tobacco) as a model...

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Detalles Bibliográficos
Autores: Dellen, Babette, Scharr, Hanno, Torras, Carme|||0000-0002-2933-398X
Tipo de recurso: artículo
Fecha de publicación:2015
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/82651
Acceso en línea:https://hdl.handle.net/2117/82651
https://dx.doi.org/10.1109/TCBB.2015.2404810
Access Level:acceso abierto
Palabra clave:computer vision
robot vision
Classificació INSPEC::Pattern recognition::Computer vision
Àrees temàtiques de la UPC::Informàtica::Robòtica
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oai_identifier_str oai:upcommons.upc.edu:2117/82651
network_acronym_str ES
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repository_id_str
spelling Growth signatures of rosette plants from time-lapse videoDellen, BabetteScharr, HannoTorras, Carme|||0000-0002-2933-398Xcomputer visionrobot visionClassificació INSPEC::Pattern recognition::Computer visionÀrees temàtiques de la UPC::Informàtica::RobòticaPlant growth is a dynamic process, and the precise course of events during early plant development is of major interest for plant research. In this work, we investigate the growth of rosette plants by processing time-lapse videos of growing plants, where we use Nicotiana tabacum (tobacco) as a model plant. In each frame of the video sequences, potential leaves are detected using a leaf-shape model. These detections are prone to errors due to the complex shape of plants and their changing appearance in the image, depending on leaf movement, leaf growth, and illumination conditions. To cope with this problem, we employ a novel graph-based tracking algorithm which can bridge gaps in the sequence by linking leaf detections across a range of neighboring frames. We use the overlap of fitted leaf models as a pairwise similarity measure, and forbid graph edges that would link leaf detections within a single frame. We tested the method on a set of tobacco-plant growth sequences, and could track the first leaves of the plant, including partially or temporarily occluded ones, along complete sequences, demonstrating the applicability of the method to automatic plant growth analysis. All seedlings displayed approximately the same growth behavior, and a characteristic growth signature was found.Peer Reviewed20152015-01-0120162016-02-05journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/82651https://dx.doi.org/10.1109/TCBB.2015.2404810reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengEuropean Commission http://dx.doi.org/10.13039/100011102 Seventh Framework Programme 247947 Gardening with a Cognitive Systemopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/826512026-05-27T15:37:01Z
dc.title.none.fl_str_mv Growth signatures of rosette plants from time-lapse video
title Growth signatures of rosette plants from time-lapse video
spellingShingle Growth signatures of rosette plants from time-lapse video
Dellen, Babette
computer vision
robot vision
Classificació INSPEC::Pattern recognition::Computer vision
Àrees temàtiques de la UPC::Informàtica::Robòtica
title_short Growth signatures of rosette plants from time-lapse video
title_full Growth signatures of rosette plants from time-lapse video
title_fullStr Growth signatures of rosette plants from time-lapse video
title_full_unstemmed Growth signatures of rosette plants from time-lapse video
title_sort Growth signatures of rosette plants from time-lapse video
dc.creator.none.fl_str_mv Dellen, Babette
Scharr, Hanno
Torras, Carme|||0000-0002-2933-398X
author Dellen, Babette
author_facet Dellen, Babette
Scharr, Hanno
Torras, Carme|||0000-0002-2933-398X
author_role author
author2 Scharr, Hanno
Torras, Carme|||0000-0002-2933-398X
author2_role author
author
dc.subject.none.fl_str_mv computer vision
robot vision
Classificació INSPEC::Pattern recognition::Computer vision
Àrees temàtiques de la UPC::Informàtica::Robòtica
topic computer vision
robot vision
Classificació INSPEC::Pattern recognition::Computer vision
Àrees temàtiques de la UPC::Informàtica::Robòtica
description Plant growth is a dynamic process, and the precise course of events during early plant development is of major interest for plant research. In this work, we investigate the growth of rosette plants by processing time-lapse videos of growing plants, where we use Nicotiana tabacum (tobacco) as a model plant. In each frame of the video sequences, potential leaves are detected using a leaf-shape model. These detections are prone to errors due to the complex shape of plants and their changing appearance in the image, depending on leaf movement, leaf growth, and illumination conditions. To cope with this problem, we employ a novel graph-based tracking algorithm which can bridge gaps in the sequence by linking leaf detections across a range of neighboring frames. We use the overlap of fitted leaf models as a pairwise similarity measure, and forbid graph edges that would link leaf detections within a single frame. We tested the method on a set of tobacco-plant growth sequences, and could track the first leaves of the plant, including partially or temporarily occluded ones, along complete sequences, demonstrating the applicability of the method to automatic plant growth analysis. All seedlings displayed approximately the same growth behavior, and a characteristic growth signature was found.
publishDate 2015
dc.date.none.fl_str_mv 2015
2015-01-01
2016
2016-02-05
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/82651
https://dx.doi.org/10.1109/TCBB.2015.2404810
url https://hdl.handle.net/2117/82651
https://dx.doi.org/10.1109/TCBB.2015.2404810
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://dx.doi.org/10.13039/100011102 Seventh Framework Programme 247947 Gardening with a Cognitive System
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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