Probability mapping images in dynamic speckle classification

Autores
Passoni, Isabel; Rabal, Hector Jorge; Meschino, Gustavo; Trivi, Marcelo
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We propose the use of a learning procedure to identify regions of similar dynamics in speckle image sequences that includes more than one descriptor. This procedure is based on the application of a naïve Bayes statistical classifier comprising the use of several descriptors. The class frontiers can be depicted so that the proportion of identified regions may be measured. To demonstrate the results, assembly of an RGB image, where each plane (R, G, and B) is associated with a particular region (class), was labeled according to its biospeckle dynamics. A high brightness in one color means a high probability of the pixel belonging to the corresponding class, and vice versa.
Fil: Passoni, Isabel. Universidad Nacional de La Plata. Facultad de Ingenieria; Argentina
Fil: Rabal, Hector Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Centro de Investigaciones Opticas (i); Argentina
Fil: Meschino, Gustavo. Universidad Nacional de La Plata. Facultad de Ingenieria; Argentina
Fil: Trivi, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Centro de Investigaciones Opticas (i); Argentina
Materia
Dynamic Speckle
Neural Network
Naive Bayes
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/7437
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/7437
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Probability mapping images in dynamic speckle classificationPassoni, IsabelRabal, Hector JorgeMeschino, GustavoTrivi, MarceloDynamic SpeckleNeural NetworkNaive Bayeshttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1We propose the use of a learning procedure to identify regions of similar dynamics in speckle image sequences that includes more than one descriptor. This procedure is based on the application of a naïve Bayes statistical classifier comprising the use of several descriptors. The class frontiers can be depicted so that the proportion of identified regions may be measured. To demonstrate the results, assembly of an RGB image, where each plane (R, G, and B) is associated with a particular region (class), was labeled according to its biospeckle dynamics. A high brightness in one color means a high probability of the pixel belonging to the corresponding class, and vice versa.Fil: Passoni, Isabel. Universidad Nacional de La Plata. Facultad de Ingenieria; ArgentinaFil: Rabal, Hector Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Centro de Investigaciones Opticas (i); ArgentinaFil: Meschino, Gustavo. Universidad Nacional de La Plata. Facultad de Ingenieria; ArgentinaFil: Trivi, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Centro de Investigaciones Opticas (i); ArgentinaOptical Society of America2013-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/7437Passoni, Isabel; Rabal, Hector Jorge; Meschino, Gustavo; Trivi, Marcelo; Probability mapping images in dynamic speckle classification; Optical Society of America; Applied Optics; 52; 4; 2-2013; 726-7331559-128Xenginfo:eu-repo/semantics/altIdentifier/url/https://www.osapublishing.org/ao/abstract.cfm?uri=ao-52-4-726info:eu-repo/semantics/altIdentifier/doi/10.1364/AO.52.000726info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:43:52Zoai:ri.conicet.gov.ar:11336/7437instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:43:53.106CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Probability mapping images in dynamic speckle classification
title Probability mapping images in dynamic speckle classification
spellingShingle Probability mapping images in dynamic speckle classification
Passoni, Isabel
Dynamic Speckle
Neural Network
Naive Bayes
title_short Probability mapping images in dynamic speckle classification
title_full Probability mapping images in dynamic speckle classification
title_fullStr Probability mapping images in dynamic speckle classification
title_full_unstemmed Probability mapping images in dynamic speckle classification
title_sort Probability mapping images in dynamic speckle classification
dc.creator.none.fl_str_mv Passoni, Isabel
Rabal, Hector Jorge
Meschino, Gustavo
Trivi, Marcelo
author Passoni, Isabel
author_facet Passoni, Isabel
Rabal, Hector Jorge
Meschino, Gustavo
Trivi, Marcelo
author_role author
author2 Rabal, Hector Jorge
Meschino, Gustavo
Trivi, Marcelo
author2_role author
author
author
dc.subject.none.fl_str_mv Dynamic Speckle
Neural Network
Naive Bayes
topic Dynamic Speckle
Neural Network
Naive Bayes
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We propose the use of a learning procedure to identify regions of similar dynamics in speckle image sequences that includes more than one descriptor. This procedure is based on the application of a naïve Bayes statistical classifier comprising the use of several descriptors. The class frontiers can be depicted so that the proportion of identified regions may be measured. To demonstrate the results, assembly of an RGB image, where each plane (R, G, and B) is associated with a particular region (class), was labeled according to its biospeckle dynamics. A high brightness in one color means a high probability of the pixel belonging to the corresponding class, and vice versa.
Fil: Passoni, Isabel. Universidad Nacional de La Plata. Facultad de Ingenieria; Argentina
Fil: Rabal, Hector Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Centro de Investigaciones Opticas (i); Argentina
Fil: Meschino, Gustavo. Universidad Nacional de La Plata. Facultad de Ingenieria; Argentina
Fil: Trivi, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Centro de Investigaciones Opticas (i); Argentina
description We propose the use of a learning procedure to identify regions of similar dynamics in speckle image sequences that includes more than one descriptor. This procedure is based on the application of a naïve Bayes statistical classifier comprising the use of several descriptors. The class frontiers can be depicted so that the proportion of identified regions may be measured. To demonstrate the results, assembly of an RGB image, where each plane (R, G, and B) is associated with a particular region (class), was labeled according to its biospeckle dynamics. A high brightness in one color means a high probability of the pixel belonging to the corresponding class, and vice versa.
publishDate 2013
dc.date.none.fl_str_mv 2013-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/7437
Passoni, Isabel; Rabal, Hector Jorge; Meschino, Gustavo; Trivi, Marcelo; Probability mapping images in dynamic speckle classification; Optical Society of America; Applied Optics; 52; 4; 2-2013; 726-733
1559-128X
url http://hdl.handle.net/11336/7437
identifier_str_mv Passoni, Isabel; Rabal, Hector Jorge; Meschino, Gustavo; Trivi, Marcelo; Probability mapping images in dynamic speckle classification; Optical Society of America; Applied Optics; 52; 4; 2-2013; 726-733
1559-128X
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.osapublishing.org/ao/abstract.cfm?uri=ao-52-4-726
info:eu-repo/semantics/altIdentifier/doi/10.1364/AO.52.000726
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Optical Society of America
publisher.none.fl_str_mv Optical Society of America
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613380839571456
score 13.069144

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