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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/7437
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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 Técnicas2025-09-03T09:49:43Zoai: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:34982025-09-03 09:49:43.54CONICET 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 |
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1842268990736433152 |
score |
13.13397 |