Characterization of spatial–temporal patterns in dynamic speckle sequences using principal component analysis
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2016
Authors
Grumel, Eduardo
Cap, Nelly Lucía
Trivi, Marcelo
Rabal, Héctor
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SPIE
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Abstract
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.
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En abierto en la web del editor.
Received Mar. 9, 2016; accepted for publication May 6, 2016; published online Jun. 7, 2016.
This paper was a derivation of our previous conference contribution titled “Characterization of dynamic speckle sequences using principal component analysis and image descriptors.”