478 research outputs found
"A Margarita Debayle", Sin Datar
No full textabstract: Handwritten poem composed by Rubén Darío.The original Rubén Darío Papers 1882-1945 (MSS-339) are located at ASU Libraries Archives & Special Collections. For more information about visiting the collection see http://hdl.handle.net/2286/L.A.0.The first page has the title "A Margarita Debayle" and Ruben Dario's name. Likewise the odd numbers have written the title as well as Ruben Dario's name.Margarita Debayle (July 4, 1900 - December 19, 1983) was Luis H. Debayle's daughter, a friend of Rubén Darío.All pages are numerated in roman numerals
Recibo de Rubén Darío para L. H. Debayle, 1908 Octubre 10
No full textabstract: The receipt is an acknowledgment for an amount of 2,000 Pesetas to Luis H. Debayle. At the time of this receipt, Debayle was the Mexican Consul in France. Rubén Darío was in Madrid when this receipt was written.The original Rubén Darío Papers 1882-1945 (MSS-339) are located at ASU Libraries Archives & Special Collections. For more information about visiting the collection see http://hdl.handle.net/2286/L.A.0.Luis H. Debayle (1865 - 1938) was a recognized and prestigious Nicaraguan doctor. He had a close friendship with Rubén Darío
“A Margarita Debayle”: en los 100 años de un apólogo memorable
No full textOne of the nine poems that Rubén Darío wrote in Nicaragua during his tropical intermezzo –between November 27, 1907 and April 3, 1908– was “A Margarita Debayle”, dated in “The Bay of Corinth (Nicaragua). Island of Cardón, March 20”, according to the footnote to its publication in the Diario de Granada with the title of “Cielo y mar. Poema (A Margarita Debayle)”. A hundred years have passed. However, this anniversary passed unnoticed, thus justifying this study.Uno de los nueve poemas que Rubén Darío escribió en Nicaragua –durante su intermezzo tropical entre el 27 noviembre, 1907 y el 3 de abril, 1908– fue “A Margarita Debayle”, fechado en “Bahía de Corinto (Nicaragua). Isla del Cardón, marzo 20 de 1908-2008”; así consta al pie de su publicación en el Diario de Granada (año II, num.526, p.1) con el título de “Cielo y mar. Poema. (A Margarita Debayle)”. Cumplió, pues, cien años. Sin embargo, esa efeméride pasó inadvertida. De ahí que haya motivado este análisis
General Adaptive Neighborhood Image Processing for biomedical applications
No full textIn biomedical imaging, the image processing techniques using spatially invariant transformations, with fixed operational windows, give efficient and compact computing structures, with the conventional separation between data and operations. Nevertheless, these operators have several strong drawbacks, such as removing significant details, changing some meaningful parts of large objects, and creating artificial patterns. This kind of approaches is generally not sufficiently relevant for helping the biomedical professionals to perform accurate diagnosis and therapy by using image processing techniques. Alternative approaches addressing context-dependent processing have been proposed with the introduction of spatially-adaptive operators (Bouannaya and Schonfeld, 2008; Ciuc et al., 2000; Gordon and Rangayyan, 1984;Maragos and Vachier, 2009; Roerdink, 2009; Salembier, 1992), where the adaptive concept results from the spatial adjustment of the sliding operational window. A spatially-adaptive image processing approach implies that operators will no longer be spatially invariant, but must vary over the whole image with adaptive windows, taking locally into account the image context by involving the geometrical, morphological or radiometric aspects. Nevertheless, most of the adaptive approaches require a priori or extrinsic informations on the image for efficient processing and analysis. An original approach, called General Adaptive Neighborhood Image Processing (GANIP), has been introduced and applied in the past few years by Debayle & Pinoli (2006a;b); Pinoli and Debayle (2007). This approach allows the building of multiscale and spatially adaptive image processing transforms using context-dependent intrinsic operational windows. With the help of a specified analyzing criterion (such as luminance, contrast, ...) and of the General Linear Image Processing (GLIP) (Oppenheim, 1967; Pinoli, 1997a), such transforms perform a more significant spatial and radiometric analysis. Indeed, they take intrinsically into account the local radiometric, morphological or geometrical characteristics of an image, and are consistent with the physical (transmitted or reflected light or electromagnetic radiation) and/or physiological (human visual perception) settings underlying the image formation processes. The proposed GAN-based transforms are very useful and outperforms several classical or modern techniques (Gonzalez and Woods, 2008) - such as linear spatial transforms, frequency noise filtering, anisotropic diffusion, thresholding, region-based transforms - used for image filtering and segmentation (Debayle and Pinoli, 2006b; 2009a; Pinoli and Debayle, 2007). This book chapter aims to first expose the fundamentals of the GANIP approach (Section 2) by introducing the GLIP frameworks, the General Adaptive Neighborhood (GAN) sets and two kinds of GAN-based image transforms: the GAN morphological filters and the GAN Choquet filters. Thereafter in Section 3, several GANIP processes are illustrated in the fields of image restoration, image enhancement and image segmentation on practical biomedical application examples. Finally, Section 4 gives some conclusions and prospects of the proposed GANIP approach
General Adaptive Neighborhood Image Processing for biomedical applications
No full textIn biomedical imaging, the image processing techniques using spatially invariant transformations, with fixed operational windows, give efficient and compact computing structures, with the conventional separation between data and operations. Nevertheless, these operators have several strong drawbacks, such as removing significant details, changing some meaningful parts of large objects, and creating artificial patterns. This kind of approaches is generally not sufficiently relevant for helping the biomedical professionals to perform accurate diagnosis and therapy by using image processing techniques. Alternative approaches addressing context-dependent processing have been proposed with the introduction of spatially-adaptive operators (Bouannaya and Schonfeld, 2008; Ciuc et al., 2000; Gordon and Rangayyan, 1984;Maragos and Vachier, 2009; Roerdink, 2009; Salembier, 1992), where the adaptive concept results from the spatial adjustment of the sliding operational window. A spatially-adaptive image processing approach implies that operators will no longer be spatially invariant, but must vary over the whole image with adaptive windows, taking locally into account the image context by involving the geometrical, morphological or radiometric aspects. Nevertheless, most of the adaptive approaches require a priori or extrinsic informations on the image for efficient processing and analysis. An original approach, called General Adaptive Neighborhood Image Processing (GANIP), has been introduced and applied in the past few years by Debayle & Pinoli (2006a;b); Pinoli and Debayle (2007). This approach allows the building of multiscale and spatially adaptive image processing transforms using context-dependent intrinsic operational windows. With the help of a specified analyzing criterion (such as luminance, contrast, ...) and of the General Linear Image Processing (GLIP) (Oppenheim, 1967; Pinoli, 1997a), such transforms perform a more significant spatial and radiometric analysis. Indeed, they take intrinsically into account the local radiometric, morphological or geometrical characteristics of an image, and are consistent with the physical (transmitted or reflected light or electromagnetic radiation) and/or physiological (human visual perception) settings underlying the image formation processes. The proposed GAN-based transforms are very useful and outperforms several classical or modern techniques (Gonzalez and Woods, 2008) - such as linear spatial transforms, frequency noise filtering, anisotropic diffusion, thresholding, region-based transforms - used for image filtering and segmentation (Debayle and Pinoli, 2006b; 2009a; Pinoli and Debayle, 2007). This book chapter aims to first expose the fundamentals of the GANIP approach (Section 2) by introducing the GLIP frameworks, the General Adaptive Neighborhood (GAN) sets and two kinds of GAN-based image transforms: the GAN morphological filters and the GAN Choquet filters. Thereafter in Section 3, several GANIP processes are illustrated in the fields of image restoration, image enhancement and image segmentation on practical biomedical application examples. Finally, Section 4 gives some conclusions and prospects of the proposed GANIP approach
3-D Mapping of the Seismic Attenuation in the Upper Mantle
No full textL'objectif de cette thèse est de construire un modèle d'atténuation sismique du manteau supérieur dela Terre en utilisant un jeu de données original construit par Debayle et Ricard (2012). Ce jeu dedonnées est l'un des plus complet au monde (plus de 375 000 sismogrammes analysés pour extrairel'atténuation et la vitesse de phase du mode fondamental et des cinq premiers harmoniques des ondesde Rayleigh).Les mesures d'atténuation sont tout d'abord traitées pour extraire les effets de l'expansion géométriqueet de la focalisation, minimiser les effets d'erreurs sur la source, écarter les mesures incertaines etregrouper les mesures redondantes. Elles sont ensuite régionalisées pour obtenir des cartes desvariations latérales de l'atténuation des ondes de Rayleigh pour chaque mode et chaque période. Ladernière étape est l'inversion en profondeur des cartes. Elle permet d'obtenir QsADR17, un modèle 3Dde l'atténuation des ondes S dans le manteau supérieur.QsADR17 est corrélé avec la tectonique de surface jusqu'à 200 km de profondeur, avec une faibleatténuation sous les continents et une forte atténuation sous les océans. Des anomalies de forteatténuation sont observées jusqu'à 150~km de profondeur sous les rides océaniques, et persistent à plusgrande profondeur jusque dans la zone de transition sous la plupart des points chauds. La présence delarges anomalies atténuantes situées à 150 km de profondeur sous l'océan Pacifique suggère queplusieurs panaches thermiques viennent s'étaler dans l'asthénosphère. Nous avons également détecté laprésence d'hétérogénéités de composition à la base des cratons et dans un certain nombre de régionsactives.The aim of this study is to build a 3-D attenuation model of Earth's upper-mantle using a unique datasetbuilt by Debayle & Ricard (2012). This dataset is among the largest in the world: more than 375,000seismograms were analyzed to extract Rayleigh-wave attenuation and velocity measurements for thefondamental mode and the five first harmonics between 40 and 240 s periods.First, attenuation measurements are processed to extract the effects of geometrical attenuation and offocusing and defocusing, in order to minimize the influence of errors on the seismic source, to avoidpotentially incorrect data, and to cluster redondant measurements. Then, measurements are regionalizedto obtain Rayleigh-wave maps for each mode and each period. The last step is the inversion of thesemaps to obtain the depth dependent attenuation. Eventually, we obtain QsADR17, a 3-D model of Swaveattenuation in the upper mantle.QsADR17 is correlated with surface tectonics down to 200 km depth, with low attenuation under thecontinents and high attenuation under the oceans. High-attenuation anomalies are found under oceanicridges down to 150~km depth, and under most of the hotspots at larger depth down to the transitionzone. A large high-attenuation anomaly at 150~km depth under the Pacific ocean suggest that thermalplumes pound into the asthenosphere. We also detect compositional heterogeneities at the base of thecratons and in active areas
A global shear velocity model of the upper mantle from fundamental and higher Rayleigh mode measurements
No full textInternational 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)
Image Processing, Analysis and Modeling of Particle Populations
No full textConférence 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)
Rayleigh wave phase velocity and error maps up to the fifth overtone
No full textInternational audienceWe present a global data set of phase velocity maps for Rayleigh waves, with their errors. These maps are obtained from the tomographic inversion of phase velocity curves measured in the period range 40–250 s by Debayle and Ricard (2012), completed with new measurements at longer periods, between 150 and 360 s. The full data set includes ∼22,000,000 phase velocity measurements combined to build 60 phase velocity maps covering the period range 40–360 s for the fundamental mode and up to the fifth overtone. Each phase velocity map is provided with its a posteriori error, resulting in a unique data set which can be combined with other seismic measurements (surface waves, normal modes, and body waves) in regional and global tomographic studies. A preliminary inversion of this data set shows that it provides constraints on the shear velocity structure down to 1000 km depth
Image Processing, Analysis and Modeling of Particle Populations
No full textConférence invitée de Johan Debayle, centre SPIN, LGF UMR CNRS 5307, en qualité de “Invited Talk“.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 first part of this talk will be focused on specific geometrical and morphometrical descriptors giving information on the size, shape and spatial distribution of the particles. They have a compact representation with good mathematical properties and are easy to compute. They are based on integral geometry, shape diagrams and computational geometry. The second part of this talk will 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). Some conclusions and prospects will be finally given
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