Characterization of dynamic speckle sequences using principal component analysis and image descriptors
Speckle is being used as a characterization tool for the analysis of the dynamic of slow varying phenomena occurring in biological and industrial samples. The retrieved data takes the form of a sequence of speckle images. The analysis of these images should reveal the inner dynamic of the biological...
| Autores: | , , , , , |
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| Tipo de documento: | capítulo de livro |
| Data de publicação: | 2015 |
| País: | España |
| Recursos: | Universidad Complutense de Madrid (UCM) |
| Repositório: | Docta Complutense |
| Idioma: | inglês |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/24884 |
| Acesso em linha: | https://hdl.handle.net/20.500.14352/24884 |
| Access Level: | Acceso aberto |
| Palavra-chave: | 537.533.3 535 535.374 dynamic speckle Fujii Generalized Dierences LASCA Principal components analysis Óptica (Física) Optoelectrónica Láseres 2209.19 Óptica Física 2209.10 Láseres |
| Resumo: | Speckle is being used as a characterization tool for the analysis of the dynamic of slow varying phenomena occurring in biological and industrial samples. The retrieved data takes the form of a sequence of speckle images. The analysis of these images should reveal the inner dynamic of the biological or physical process taking place in the sample. Very recently, it has been shown that principal component analysis is able to split the original data set in a collection of classes. These classes can be related with the dynamic of the observed phenomena. At the same time, statistical descriptors of biospeckle images have been 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, principal component analysis requires longer computation time but the results contain more information related with spatial-temporal pattern that can be identified with physical process. This contribution merges both descriptions and uses principal component analysis as a pre-processing tool to obtain a collection of filtered images where a simpler statistical descriptor can be calculated. The method has been applied to slow-varying biological and industrial processes |
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