A user-friendly interactive framework for unsteady fluid flow segmentation and visualization
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s12650-018-0474-6 http://hdl.handle.net/11449/170652 |
Resumo: | Abstract: While vector fields are essential to simulate a large amount of natural phenomena, the difficulty to identify patterns and predict behaviors makes the visual segmentation in simulations an attractive and powerful tool. In this paper, we present a novel user-steered segmentation framework to cope with steady as well as unsteady vector fields on fluid flow simulations. Given a discrete vector field, our approach extracts multi-valued features from the field by exploiting its streamline structures so that these features are mapped to a visual space through a multidimensional projection technique. From an easy-to-handle interface, the user can interact with the projected data so as to partition and explore the most relevant vector features in a guidance frame of the simulation. Besides navigating and visually mining structures of interest, the interactivity with the projected data also allows the user to progressively enhance the segmentation result according to his insights. Finally, to successfully deal with unsteady simulations, the segments previously annotated by the user are used as a training set for a Support Vector Machine approach that classifies the remaining frames in the flow. We attest the effectiveness and versatility of our methodology throughout a set of classical physical-inspired applications on fluid flow simulations as depicted in the experiment results section. Graphical Abstract: [Figure not available: see fulltext.]. |
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Repositório Institucional da UNESP |
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A user-friendly interactive framework for unsteady fluid flow segmentation and visualizationFlow segmentationInteractive toolsMachine learningTime-varying visualizationVector fieldAbstract: While vector fields are essential to simulate a large amount of natural phenomena, the difficulty to identify patterns and predict behaviors makes the visual segmentation in simulations an attractive and powerful tool. In this paper, we present a novel user-steered segmentation framework to cope with steady as well as unsteady vector fields on fluid flow simulations. Given a discrete vector field, our approach extracts multi-valued features from the field by exploiting its streamline structures so that these features are mapped to a visual space through a multidimensional projection technique. From an easy-to-handle interface, the user can interact with the projected data so as to partition and explore the most relevant vector features in a guidance frame of the simulation. Besides navigating and visually mining structures of interest, the interactivity with the projected data also allows the user to progressively enhance the segmentation result according to his insights. Finally, to successfully deal with unsteady simulations, the segments previously annotated by the user are used as a training set for a Support Vector Machine approach that classifies the remaining frames in the flow. We attest the effectiveness and versatility of our methodology throughout a set of classical physical-inspired applications on fluid flow simulations as depicted in the experiment results section. Graphical Abstract: [Figure not available: see fulltext.].Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Universidade de São Paulo (USP) ICMCUniversidade Estadual Paulista (UNESP)Universidade Federal de Mato Grosso do Sul (UFMS) FACOMUniversidade Estadual Paulista (UNESP)FAPESP: 2013/07375-0FAPESP: 2014/16857-0CAPES: 88881.133553/2016-01Universidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Universidade Federal de Mato Grosso do Sul (UFMS)Motta, DaniloCasaca, Wallace [UNESP]Pagliosa, PauloPaiva, Afonso2018-12-11T16:51:51Z2018-12-11T16:51:51Z2018-08-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article625-636application/pdfhttp://dx.doi.org/10.1007/s12650-018-0474-6Journal of Visualization, v. 21, n. 4, p. 625-636, 2018.1875-89751343-8875http://hdl.handle.net/11449/17065210.1007/s12650-018-0474-62-s2.0-850418222492-s2.0-85041822249.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Visualization0,267info:eu-repo/semantics/openAccess2023-11-04T06:09:37Zoai:repositorio.unesp.br:11449/170652Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:53:43.004992Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A user-friendly interactive framework for unsteady fluid flow segmentation and visualization |
title |
A user-friendly interactive framework for unsteady fluid flow segmentation and visualization |
spellingShingle |
A user-friendly interactive framework for unsteady fluid flow segmentation and visualization Motta, Danilo Flow segmentation Interactive tools Machine learning Time-varying visualization Vector field |
title_short |
A user-friendly interactive framework for unsteady fluid flow segmentation and visualization |
title_full |
A user-friendly interactive framework for unsteady fluid flow segmentation and visualization |
title_fullStr |
A user-friendly interactive framework for unsteady fluid flow segmentation and visualization |
title_full_unstemmed |
A user-friendly interactive framework for unsteady fluid flow segmentation and visualization |
title_sort |
A user-friendly interactive framework for unsteady fluid flow segmentation and visualization |
author |
Motta, Danilo |
author_facet |
Motta, Danilo Casaca, Wallace [UNESP] Pagliosa, Paulo Paiva, Afonso |
author_role |
author |
author2 |
Casaca, Wallace [UNESP] Pagliosa, Paulo Paiva, Afonso |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade de São Paulo (USP) Universidade Estadual Paulista (Unesp) Universidade Federal de Mato Grosso do Sul (UFMS) |
dc.contributor.author.fl_str_mv |
Motta, Danilo Casaca, Wallace [UNESP] Pagliosa, Paulo Paiva, Afonso |
dc.subject.por.fl_str_mv |
Flow segmentation Interactive tools Machine learning Time-varying visualization Vector field |
topic |
Flow segmentation Interactive tools Machine learning Time-varying visualization Vector field |
description |
Abstract: While vector fields are essential to simulate a large amount of natural phenomena, the difficulty to identify patterns and predict behaviors makes the visual segmentation in simulations an attractive and powerful tool. In this paper, we present a novel user-steered segmentation framework to cope with steady as well as unsteady vector fields on fluid flow simulations. Given a discrete vector field, our approach extracts multi-valued features from the field by exploiting its streamline structures so that these features are mapped to a visual space through a multidimensional projection technique. From an easy-to-handle interface, the user can interact with the projected data so as to partition and explore the most relevant vector features in a guidance frame of the simulation. Besides navigating and visually mining structures of interest, the interactivity with the projected data also allows the user to progressively enhance the segmentation result according to his insights. Finally, to successfully deal with unsteady simulations, the segments previously annotated by the user are used as a training set for a Support Vector Machine approach that classifies the remaining frames in the flow. We attest the effectiveness and versatility of our methodology throughout a set of classical physical-inspired applications on fluid flow simulations as depicted in the experiment results section. Graphical Abstract: [Figure not available: see fulltext.]. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-12-11T16:51:51Z 2018-12-11T16:51:51Z 2018-08-01 |
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/10.1007/s12650-018-0474-6 Journal of Visualization, v. 21, n. 4, p. 625-636, 2018. 1875-8975 1343-8875 http://hdl.handle.net/11449/170652 10.1007/s12650-018-0474-6 2-s2.0-85041822249 2-s2.0-85041822249.pdf |
url |
http://dx.doi.org/10.1007/s12650-018-0474-6 http://hdl.handle.net/11449/170652 |
identifier_str_mv |
Journal of Visualization, v. 21, n. 4, p. 625-636, 2018. 1875-8975 1343-8875 10.1007/s12650-018-0474-6 2-s2.0-85041822249 2-s2.0-85041822249.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal of Visualization 0,267 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
625-636 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_ |
1808128717594034176 |