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.
Highlights
Speckle analysis and speckle interferometry have been used to obtain deformations, topography, and roughness characteristics of a wide variety of samples
We briefly present the results obtained from Principal component analysis (PCA) and how they can explain the inner dynamics of the data
When analyzing dynamic speckle sequences, this power spectral density (PSD) follows a 1∕f pattern, where PSDðωÞ 1⁄4 A1∕f∕ωα, being α ≃ 1.12 For classes P2 and P3, it is not possible to define a single frequency for each eigenvector
Summary
Speckle analysis and speckle interferometry have been used to obtain deformations, topography, and roughness characteristics of a wide variety of samples. Artificial intelligence techniques have been incorporated, along with learning algorithms (supervised and nonsupervised), to segment and identify regions of interest within data.[2,3] there is not a unique strategy for analyzing dynamic speckle images applicable to a wide variety of situations. In this contribution, we merge together the outcomes of two well-established methods: principal component analysis (PCA) and image descriptors. Typical image descriptors are evaluated and compared with the classical dynamic speckle analysis methods applied to original and PCA-filtered sequences Using this strategy, it is possible to determine which PCA band reveals the region of interest for each descriptor. López-Alonso et al.: Characterization of spatial–temporal patterns in dynamic
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