Eduardo Grumel

Numerical model for dynamic speckle: an approach using the movement of the scatterers

Dynamic speckle or biospeckle is a phenomenon generated by laser light scattering in biological tissues and it is also present in some industrial processes where the surfaces exhibit some kind of activity. In this work we present some results of a numerical model to describe the time history of a dynamic speckle pattern considering very simplified situations. In this case we simulated only the movement of the scatterers, and the obtained results are in agreement with some of the expected from the theory and qualitatively with the experimental results outlined in previous works. Also, we tested the first order statistics of the spatial speckle, the Briers contrast of the time varying part and the moment of inertia of the co-occurrence matrix.

Dynamic speckle analysis using multivariate techniques

In this work we use principal components analysis to characterize dynamic speckle patterns. This analysis quantitatively identifies different dynamics that could be associated to physical phenomena occurring in the sample. We also found the contribution explained by each principal component, or by a group of them. The method analyzes the paint drying process over a hidden topography. It can be used for fast screening and identification of different dynamics in biological or industrial samples by means of dynamic speckle interferometry.

Characterization of spatial–temporal patterns in dynamic speckle sequences using principal component analysis

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.

Evaluation of activity images in dynamics speckle in search of objective estimators

We explore the performance of two algorithms to screen loci of equal activity in dynamic speckle images. Dynamic speckle images are currently applied to several applications in medicine, biology, agriculture and other disciplines. Nevertheless, no objective standard has been proposed so far to evaluate the performance of the algorithms, which must be then relied on subjective appreciations. We use two case studies of activity that do not bear the biologic inherent variability to test the methods: “Generalized Differences” and “Fujii”, looking for a standard to evaluate their performance in an objective way. As study cases, we use the drying of paint on an (assumed) unknown topography, namely the surface of a coin, and the activity due to pre heating a piece of paper that hides writings in the surface under the paper. A known object of simple topography is included in the image, besides of the painted coin, consisting in a paint pool where the depth is a linear function of its position. Both algorithms are applied to the images and the intensity profile of the results along the linear region of the pool activity is used to estimate the depth of the geometric topography hidden under paint in the coin. The accuracy of the result is used as a merit estimation of the corresponding algorithm. In the other experiment, a hidden dark bar printed on paper is covered with one or two paper leaves, slightly pre heated with a lamp and activity images registered and processed with both algorithms. The intensity profile of the activity images is used to estimate which method gives a better description of the bar edges images and their deterioration. Experimental results are shown.

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 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

Box fractal dimension in speckle images

In this paper, we propose a generalization of the box fractal dimension in images by considering the curve obtained from its value as a function of the binarization threshold. This curve can be used to describe speckle patterns. We show some examples of both objective simulated and experimental and subjective speckle in some cases of interest. The concept can be extended for all types of images.

A geometric description of the spatial coherence and Babinet´sp like principle for the fringe visibility

Fil: Rabal, Hector Jorge. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico La Plata. Centro de Investigaciones Opticas (i); Argentina. Provincia de Buenos Aires. Gobernacion. Comision de Investigaciones Cientificas; Argentina. Universidad Nacional de La Plata. Facultad de Ingenieria; Argentina

An introductory approach to the concept of spatial coherence

The concept of spatial coherence is usually hard to be understood the first time that it is studied. We propose here a geometric description that does not contain mathematical difficulties and permits to understand how a Young´s Fringes system is obtained with a source not spatially coherent. It is based in a very simple experiment that permits the detection of spatial coherence in a scene. Experimental results are shown

Numerical model for dynamic speckle: preliminary results

The dynamic speckle is a phenomenon generated by laser light scattering in biological tissues and it is also present in some industrial processes where the surfaces exhibit some kind of activity. In this work we present preliminary results of a numerical model to describe the time history of a dynamic speckle pattern considering very simplified situations.