First Approach for the Modelling of the Electric Field Surrounding a Piezoelectric Transformer in View of Plasma Generation

This paper is about an open multi-physics modelling problem resulting from recent investigations into plasma generation by piezoelectric transformers. In this first approach, the electric field distribution surrounding the transformer is studied according to a weak coupling formulation. Electric potential distribution views obtained numerically are compared to real views of plasma generation observed experimentally

Fast image and video segmentation based on alpha-tree multiscale representation

International audienceIn this paper, we propose to rely on a recent image representation model, namely the α-tree, to achieve efficient segmentation of images and videos. The α-tree is a multiscale representation of an image, based on its quasi-flat zones. An in-depth study of this tree reveals some interesting features of image pixels and regions. These features are then used in the design of both automatic and interactive segmentation algorithms. Interactivity is achieved thanks to a new and efficient implementation scheme. Experiments on the Berkeley Segmentation Dataset lead to very promising results

Hyperspectral image classification from multiscale description with constrained connectivity and metric learning

International audienceMapping of remote sensing data is usually done through image classification. For hyperspectral images, the classification process often relies only on the spectral signature of each single pixel. Nevertheless, combining spatial and spectral features has been a promising way for accuracy improvement. We address here this problem by computing spectral features from spatially identified regions, sampled from a hierarchical image representation, namely α-tree, built with prior knowledge. The sampling of the tree nodes (i.e., regions) is based on the paradigm of constrained connectivity and the global range criterion. In this paper, we extend this criterion to hy-perspectral data and apply it to our knowledge-based α-tree. Our results show an improvement of pixelwise classification accuracy over spectral features only

Efficient Schemes for Computing α-tree Representations

International audienceHierarchical image representations have been addressed by various models by the past, the max-tree being probably its best representative within the scope of Mathematical Morphology. However, the max-tree model requires to impose an ordering relation between pixels, from the lowest values (root) to the highest (leaves). Recently, the α-tree model has been introduced to avoid such an ordering. Indeed, it relies on image quasi-flat zones, and as such focuses on local dissimilarities. It has led to successful attempts in remote sensing and video segmentation. In this paper, we deal with the problem of α-tree computation, and propose several efficient schemes which help to ensure real-time (or near-real time) morphological image processing

Hyperspectral image representation through alpha-trees

International audienceα-trees provide a hierarchical representation of an image into partitions of regions with increasing heterogeneity. This model, inspired from the single-linkage paradigm, has recently been revisited for grayscale images and has been successfully used in the field of remote sensing. This article shows how this representation can be adapted to more complex data here hyperspectral images, according to different strategies. We know that the measure of distance between two neighbouring pixels is a key element for the quality of the underlying tree, but usual metrics are not satisfying. We show here that a relevant solution to understand hyperspectral data relies on the prior learning of the metric to be used and the exploitation of domain knowledge

Belief functions contextual discounting and canonical decompositions

AbstractIn this article, the contextual discounting of a belief function, a classical discounting generalization, is extended and its particular link with the canonical disjunctive decomposition is highlighted. A general family of correction mechanisms allowing one to weaken the information provided by a source is then introduced, as well as the dual of this family allowing one to strengthen a belief function

La Société d’Agriculture, Commerce, Sciences et Arts du département de la Marne (1798-2015) : deux siècles d’existence d’une société savante

Héritière de l’Académie de Châlons (1775), la SACSAM (1798) a continué son œuvre. Elle organise des concours et, sous l’influence du préfet Bourgeois de Jessaint, porte un intérêt particulier à l’agriculture. Au milieu du siècle des membres des professions de santé et du droit, parfois collectionneurs (Garinet, Nicaise) orientent la Société vers l’histoire de l’art. Le premier volume des Mémoires (1855-56) en témoigne. Après la guerre de 1870, l’archéologie offre des études richement illustrées. À la fin du siècle, sous l’influence des archivistes départementaux (P. Pelicier, J. Berland) les travaux historiques prennent une place plus importante avec l’exploitation des archives. Après 1914, le rythme de la publication se ralentit. Après 1941, René Gandilhon, nouvel archiviste, recrute des membres et attire des chercheurs de qualité qui maintiennent à un haut niveau la publication. Mais surtout, il forme de jeunes étudiants chercheurs aux techniques de la publication scientifique. Son disciple, Armand Ferrant, continue cette mission de responsable de la publication. À partir de 1994, les archives départementales n’assurent plus le fonctionnement de la Société, des enseignants et des personnes de bonne volonté prennent le relais, disposant des locaux légués à la Société par Madame Garinet