Image Processing, Analysis and Modeling of Particle Populations

Conférence invitée de Johan Debayle, centre SPIN, LGF UMR CNRS 5307, en qualité de “Plenary Speaker“.International audienceParticle populations are widely used in many industrial applications and fields of science from physics to biology or agronomy. In chemical engineering, in particular, it is generally desired to extract information on geometrical characteristics and on spatial distribution from 2D images of the population of particles involved in the process. For example in pharmaceutics, the size and the shape of crystals of active ingredients are known to have a considerable impact on the final quality of products, such as drugs. Hence, it is of main importance to be able to control in real time the granulometry (size and shape) of the crystals during the process. The purpose of this talk is then to show different ways (deterministic and stochastic methods) of image processing, analysis and modeling to geometrically characterize the particles from a sequence of 2-D images acquired by a camera (visualizing the particles during a particular process). The developed methods will be presented by addressing different issues: the perspective projection of the 3-D particle shape onto the image plane, the blurred appearance of unfocused particles, the degree of agglomeration or overlapping, and the random variation in size/shape of the observed particles. The methods are mainly based on image enhancement, restoration, segmentation, tracking, modeling, feature detection, stereology, stochastic geometry, pattern analysis and recognition. The methods will be particularly illustrated on real applications of crystallization processes (for pharmaceutics industry) and multiphase flow processes (for nuclear industry)

Geometrical and morphometrical tools for the inclusion analysis of metallic alloys

International audienceThe mechanical and use properties of metal alloys depend on several factors, including the amount and the geometry of impurities (inclusions). In this context, image analysis enables these inclusions to be studied from digital images acquired by various systems such as optical/electron microscopy or X-ray tomography. This paper therefore aims to present some geometrical and morphometrical tools of image analysis, in order to characterize inclusions in metal alloys. To achieve this quantification, many geometrical and morphometrical features are traditionally used to quantitatively describe a population of objects (inclusions). Integral geometry, via Minkowski’s functionals (in 2D: area, perimeter, Euler-Poincaré number), has been particularly investigated in image analysis. Nevertheless, they are sometimes insufficient for the characterization of complex microstructures (such as aggregates/agglomerates of objects). Other quantitative parameters are then necessary in order to discriminate or group different families of objects. In particular, shape diagrams are mathematical representations in the Euclidean plane for studying the morphology (shape) of objects, regardless of their size. In addition, this representation also makes it possible to analyze the evolution from one shape to another. In conclusion, image analysis using integral geometry and shape diagrams provide efficient tools with known mathematical properties to quantitatively describe inclusions (providing separate information on size and shape). The geometrical characteristics of these inclusions could thereafter be related to the mechanical properties of the metal alloys

Image Processing, Analysis and Modeling of Particle Populations

International audienceParticle populations are widely used in many industrial applications and fields of science from physics to biology or agronomy. In chemical engineering, in particular, it is generally desired to extract information on geometrical characteristics and on spatial distribution from 2D images of thepopulation of particles involved in the process. For example in pharmaceutics, the size and the shape of crystals of active ingredients are known to have a considerable impact on the final quality of products, such as drugs. Hence, it is of main importance to be able to control in real time thegranulometry (size and shape) of the crystals during the process. The purpose of this talk is then to show different ways (deterministic and stochastic methods) of image processing, analysis and modeling to geometrically characterize the particles from a sequence of 2-D images acquired by a camera (visualizing the particles during a particular process). The developed methods will be presented by addressing different issues: the perspective projection of the 3-D particle shape onto the image plane, the blurred appearance of unfocused particles, the degree of agglomeration or overlapping, and the random variation in size/shape of the observed particles. The methods are mainly based on image enhancement, restoration, segmentation, tracking, modeling, feature detection, stereology, stochastic geometry, pattern analysis and recognition. The methods will be particularly illustrated on real applications of crystallization processes (for pharmaceutics industry) and multiphase flow processes (for nuclear industry)

A global shear velocity model of the upper mantle from fundamental and higher Rayleigh mode measurements

International audienceWe present DR2012, a global SV-wave tomographic model of the upper mantle. We use an extension of the automated waveform inversion approach of Debayle (1999) which improves our mapping of the transition zone with extraction of fundamental and higher-mode information. The new approach is fully automated and has been successfully used to match approximately 375,000 Rayleigh waveforms. For each seismogram, we obtain a path average shear velocity and quality factor model, and a set of fundamental and higher-mode dispersion and attenuation curves. We incorporate the resulting set of path average shear velocity models into a tomographic inversion. In the uppermost 200 km of the mantle, SV wave heterogeneities correlate with surface tectonics. The high velocity signature of cratons is slightly shallower (approximate to 200 km) than in other seismic models. Thicker continental roots are not required by our data, but can be produced by imposing a priori a smoother model in the vertical direction. Regions deeper than 200 km show no velocity contrasts larger than +/- 1\% at large scale, except for high velocity slabs within the transition zone. Comparisons with other seismic models show that current surface wave datasets allow to build consistent models up to degrees 40 in the upper 200 km of the mantle. The agreement is poorer in the transition zone and confined to low harmonic degrees (<= 10)

Inversion of massive surface wave data sets: Model construction and resolution assessment

International audience[1] A new scheme is proposed for the inversion of surface waves using a continuous formulation of the inverse problem and the least squares criterion. Like some earlier schemes a Gaussian a priori covariance function controls the horizontal degree of smoothing in the inverted model, which minimizes some artifacts observed with spherical harmonic parameterizations. Unlike earlier schemes the new approach incorporates some sophisticated geometrical algorithms which dramatically increase computational efficiency and render possible the inversion of several tens of thousands of seismograms in few hours on a typical workstation. The new algorithm is also highly suited to parallelization which makes practical the inversion of data sets with more than 50,000 ray paths. The constraint on structural and anisotropic parameters is assessed using a new geometric approach based on Voronoi diagrams, polygonal cells covering the Earth's surface. The size of the Voronoi cells is used to give an indication of the length scale of the structures that can be resolved, while their shape provides information on the variation of azimuthal resolution. The efficiency of the scheme is illustrated with realistic uneven ray path configurations. A preliminary global tomographic model has been built for SV wave heterogeneities and azimuthal variations through the inversion of 24,124 fundamental and higher-mode Rayleigh waveforms. Our results suggest that the use of relatively short paths (<10,000 km) in a global inversion should minimize multipathing, or focusing/defocusing effects and provide lateral resolution of a few hundred kilometers across the globe

Characterization of laser-produced fast electron source for integrated simulation of fast ignition

Relativistic electron currents (∼ 10 kA µm−2) are produced by focusing an intense laser beam (I ≥ 1019W cm−2) on a solid target. Based on this mechanism, an original inertial confinement fusion scheme has been proposed which consists in heating the compressed deuterium-tritium core with a laser-produced electron beam. Experimentally the fast electron source is not well characterized and simulations of both electron generation and transport remain a difficult task. Generally, transport codes are used with a simplified fast electron source as initial condition. The fast electron spectrum is assumed to be exponential with an adjustable temperature, and the divergence is characterized by a dispersion angle. To verify these assumptions, we have performed a characterization of the laser-driven fast electron source by means of PIC simulations [1] in the cases of a planar foil and a double cone

General Adaptive Neighborhood Image Processing. Part II: Practical Applications Issues

23 pagesInternational audienceThe so-called General Adaptive Neighborhood Image Processing (GANIP) approach is presented in a two parts paper dealing respectively with its theoretical and practical aspects. The General Adaptive Neighborhood (GAN) paradigm, theoretically introduced in Part I [20], allows the building of new image processing transformations using context-dependent analysis. With the help of a specified analyzing criterion, such transformations perform a more significant spatial analysis, taking intrinsically into account the local radiometric, morphological or geometrical characteristics of the image. Moreover they are consistent with the physical and/or physiological settings of the image to be processed, using general linear image processing frameworks. In this paper, the GANIP approach is more particularly studied in the context of Mathematical Morphology (MM). The structuring elements, required for MM, are substituted by GAN-based structuring elements, fitting to the local contextual details of the studied image. The resulting morphological operators perform a really spatiallyadaptive image processing and notably, in several important and practical cases, are connected, which is a great advantage compared to the usual ones that fail to this property. Several GANIP-based results are here exposed and discussed in image filtering, image segmentation, and image enhancement. In order to evaluate the proposed approach, a comparative study is as far as possible proposed between the adaptive and usual morphological operators. Moreover, the interests to work with the Logarithmic Image Processing framework and with the 'contrast' criterion are shown through practical application examples

Caractérisation géométrique et vélocimétrique d'empilements granulaires par analyse d'image

National audienceSee http://hal.archives-ouvertes.fr/docs/00/59/27/21/ANNEX/r_9NW7X92J.pd

General Adaptive Neighborhood Image Processing. Part I: Introduction and Theoretical Aspects

30 pagesInternational audienceThe so-called General Adaptive Neighborhood Image Processing (GANIP) approach is presented in a two parts paper dealing respectively with its theoretical and practical aspects. The Adaptive Neighborhood (AN) paradigm allows the building of new image processing transformations using context-dependent analysis. Such operators are no longer spatially invariant, but vary over the whole image with ANs as adaptive operational windows, taking intrinsically into account the local image features. This AN concept is here largely extended, using well-defined mathematical concepts, to that General Adaptive Neighborhood (GAN) in two main ways. Firstly, an analyzing criterion is added within the definition of the ANs in order to consider the radiometric, morphological or geometrical characteristics of the image, allowing a more significant spatial analysis to be addressed. Secondly, general linear image processing frameworks are introduced in the GAN approach, using concepts of abstract linear algebra, so as to develop operators that are consistent with the physical and/or physiological settings of the image to be processed. In this paper, the GANIP approach is more particularly studied in the context of Mathematical Morphology (MM). The structuring elements, required for MM, are substituted by GAN-based structuring elements, fitting to the local contextual details of the studied image. The resulting transforms perform a relevant spatially-adaptive image processing, in an intrinsic manner, that is to say without a priori knowledge needed about the image structures. Moreover, in several important and practical cases, the adaptive morphological operators are connected, which is an overwhelming advantage compared to the usual ones that fail to this property