Characterization of spatial-temporal patterns in dynamic speckle sequences using principal component analysis

Speckle is being used as a characterization tool for the analysis of the dynamics of slow-varying phenomena occurring in biological and industrial samples at the surface or near-surface regions. The retrieved data take the form of a sequence of speckle images. These images contain information about...

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Detalles Bibliográficos
Autores principales: López Alonso, José Manuel, Grumel, Eduardo Emilio, Cap, Nelly Lucía, Trivi, Marcelo Ricardo, Rabal, Héctor Jorge, Alda, Javier
Formato: Articulo
Lenguaje:Inglés
Publicado: 2016
Materias:
Ingeniería
Ciencias Exactas
dynamic speckle
principal components analysis
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/85904
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-85904
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ingeniería
Ciencias Exactas
dynamic speckle
principal components analysis
spellingShingle Ingeniería
Ciencias Exactas
dynamic speckle
principal components analysis
López Alonso, José Manuel
Grumel, Eduardo Emilio
Cap, Nelly Lucía
Trivi, Marcelo Ricardo
Rabal, Héctor Jorge
Alda, Javier
Characterization of spatial-temporal patterns in dynamic speckle sequences using principal component analysis
topic_facet Ingeniería
Ciencias Exactas
dynamic speckle
principal components analysis
description Speckle is being used as a characterization tool for the analysis of the dynamics of slow-varying phenomena occurring in biological and industrial samples at the surface or near-surface regions. The retrieved data take the form of a sequence of speckle images. These images contain information about the inner dynamics of the biological or physical process taking place in the sample. Principal component analysis (PCA) is able to split the original data set into a collection of classes. These classes are related to processes showing different dynamics. In addition, statistical descriptors of speckle images are used to retrieve information on the characteristics of the sample. These statistical descriptors can be calculated in almost real time and provide a fast monitoring of the sample. On the other hand, PCA requires a longer computation time, but the results contain more information related to spatial-temporal patterns associated to the process under analysis. This contribution merges both descriptions and uses PCA as a preprocessing tool to obtain a collection of filtered images, where statistical descriptors are evaluated on each of them. The method applies to slow-varying biological and industrial processes.
format Articulo
Articulo
author López Alonso, José Manuel
Grumel, Eduardo Emilio
Cap, Nelly Lucía
Trivi, Marcelo Ricardo
Rabal, Héctor Jorge
Alda, Javier
author_facet López Alonso, José Manuel
Grumel, Eduardo Emilio
Cap, Nelly Lucía
Trivi, Marcelo Ricardo
Rabal, Héctor Jorge
Alda, Javier
author_sort López Alonso, José Manuel
title Characterization of spatial-temporal patterns in dynamic speckle sequences using principal component analysis
title_short Characterization of spatial-temporal patterns in dynamic speckle sequences using principal component analysis
title_full Characterization of spatial-temporal patterns in dynamic speckle sequences using principal component analysis
title_fullStr Characterization of spatial-temporal patterns in dynamic speckle sequences using principal component analysis
title_full_unstemmed Characterization of spatial-temporal patterns in dynamic speckle sequences using principal component analysis
title_sort characterization of spatial-temporal patterns in dynamic speckle sequences using principal component analysis
publishDate 2016
url http://sedici.unlp.edu.ar/handle/10915/85904
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AT capnellylucia characterizationofspatialtemporalpatternsindynamicspecklesequencesusingprincipalcomponentanalysis
AT trivimarceloricardo characterizationofspatialtemporalpatternsindynamicspecklesequencesusingprincipalcomponentanalysis
AT rabalhectorjorge characterizationofspatialtemporalpatternsindynamicspecklesequencesusingprincipalcomponentanalysis
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bdutipo_str Repositorios
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