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7 June 2016 Characterization of spatial–temporal patterns in dynamic speckle sequences using principal component analysis
José Manuel López-Alonso, Eduardo Grumel, Nelly Lucía Cap, Marcelo Trivi, Héctor Rabal, Javier Alda
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Optical Engineering, Vol. 55, Issue 12, 121705 (June 2016). https://doi.org/10.1117/1.OE.55.12.121705
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.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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José Manuel López-Alonso, Eduardo Grumel, Nelly Lucía Cap, Marcelo Trivi, Héctor Rabal, and Javier Alda "Characterization of spatial–temporal patterns in dynamic speckle sequences using principal component analysis," Optical Engineering 55(12), 121705 (7 June 2016). https://doi.org/10.1117/1.OE.55.12.121705
Published: 7 June 2016
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Cited by 4 scholarly publications and 1 patent.
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KEYWORDS
Principal component analysis
Speckle
Speckle pattern
Optical engineering
Biological research
Statistical analysis
Image filtering
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