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Optical Engineering • Open Access

Characterization of spatial–temporal patterns in dynamic speckle sequences using principal component analysis
Author(s): José Manuel López-Alonso; Eduardo Grumel; Nelly Lucía Cap; Marcelo Trivi; Héctor Rabal; Javier Alda

Paper Abstract

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.

Paper Details

Date Published: 7 June 2016
PDF: 8 pages
Opt. Eng. 55(12) 121705 doi: 10.1117/1.OE.55.12.121705
Published in: Optical Engineering Volume 55, Issue 12
Show Author Affiliations
José Manuel López-Alonso, Univ. Complutense de Madrid (Spain)
Eduardo Grumel, Ctr. de Investigaciones Ópticas (Argentina)
Nelly Lucía Cap, Univ. Nacional de la Plata (Argentina)
Marcelo Trivi, Univ. Nacional de Mar del Plata (Argentina)
Héctor Rabal, Univ. Nacional de la Plata (Argentina)
Javier Alda, Univ. Complutense de Madrid (Spain)


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