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International audience; Low-temperature superconductors are widely used in high-field magnets, mostly within Rutherford-type cables. The run for higher fields leads to greater forces on the conductor, which is pushed closer to its mechanical limit. Managing the higher strain and stress levels on the conductor supposes to perform simulation at the strand level, especially with strain-sensitive superconductors such as Nb3Sn. Three-dimensional models are necessary because inside of a magnet, the conductor is subject to a complex combination of axial and transverse loads. Superconducting cables are anisotropic composite structures that can comprise superconducting strands, insulation materials, stabilizing parts, porosities, etc. They have a multiscale architecture, the performance at the magnet scale being driven by the filament scale. This paper proposes a numerical approach for 3-D finite-element (FE) modeling of Rutherford cables, at the scale of the strand. The 3-D mesh of two reference cables is built with a simplified cable forming model. The accuracy of this geometrical model is assessed by comparing sections of the simulated mesh with tomographic data, using relevant criteria and specific geometry analysis tools. In this approach, the strand is considered as bimetallic, with a copper core, a superconducting bundle ring, and an outer copper ring. After mechanical 3-D FE computation on this bimetallic mesh, the strain/stress state on the local compounds of the strand (including Nb3Sn filaments) is obtained by strain projection. The strain/stress state on the Nb3Sn filaments can be used to estimate the cable current transport capability, thanks to the existing scaling laws.

International audience; In this paper, a new method for supervised hyperspectral data classification is proposed. In particular, the notion of stochastic Minimum Spanning Forest (MSF) is introduced. For a given hyperspectral image, a pixelwise classification is first performed. From this classification map, M marker maps are generated by randomly selecting pixels and labeling them as markers for the construction of Minimum Spanning Forests. The next step consists in building an MSF from each of the M marker maps. Finally, all the M realizations are aggregated with a maximum vote decision rule, in order to build the final classification map. The proposed method is tested on three different data sets of hyperspectral airborne images with different resolutions and contexts. The influence of the number of markers and of the number of realizations M on the results are investigated in experiments. The performance of the proposed method is compared to several classification techniques (both pixelwise and spectral-spatial) using standard quantitative criteria and visual qualitative evaluation.

International audience; A novel approach for vector ordering is introduced in this paper. The generic framework is based on a supervised learning formulation which leads to reduced orderings. A training set for the background and another training set for the foreground are needed as well as a supervised method to construct the ordering mapping. Two particular cases of learning techniques are considered in detail: 1) kriging-based vector ordering and 2) support vector machines-based vector ordering. These supervised orderings may then be used for the extension of mathematical morphology to vector images. In particular, in this paper, we focus on the application of morphological processing to hyperspectral images, illustrating the performance with practical examples.

International audience; This paper deals with the theory and applications of spatially-variant discrete mathematical morphology. We review and formalize the definition of spatially variant dilation/erosion and opening/closing for binary and gray-level images using exclusively the structuring function, without resorting to complement. This theoretical framework allows to build morphological operators whose structuring elements can locally adapt their shape and orientation across the dominant direction of the structures in the image. The shape and orientation of the structuring element at each pixel are extracted from the image under study: the orientation is given by means of a diffusion process of the average square gradient field, which regularizes and extends the orientation information from the edges of the objects to the homogeneous areas of the image; and the shape of the orientated structuring elements can be linear or it can be given by the distance to relevant edges of the objects. The proposed filters are used on binary and gray-level images for enhancement of anisotropic features such as coherent, flow-like structures. Results of spatially-variant erosions/dilations and openings/closings-based filters prove the validity of this theoretical sound and novel approach.

International audience; We propose in this paper to perform mathematical morphology operators in a geometric transformation of an image. As a result of this procedure, processing images with regular structuring elements in the transformed domain is equivalent to working with deformed structuring elements in the original representation. More specifically, the conversion into polar-logarithmic coordinates provides satisfying results in image analysis applied to round objects, if they are roughly origin-centered. We have illustrated the interest of the derived cyclic morphology with two pattern recognition examples: erythrocyte shape analysis and multiscale description of iris textures.

The algorithm proposed in this paper allows to automatically segment the optic disc from a fundus image. The goal is to facilitate the early detection of certain pathologies and to fully automate the process so as to avoid specialist intervention. The method proposed for the extraction of the optic disc contour is mainly based on mathematical morphology along with principal component analysis (PCA). It makes use of different operations such as generalized distance function (GDF), a variant of the watershed transformation, the stochastic watershed, and geodesic transformations. The input of the segmentation method is obtained through PCA. The purpose of using PCA is to achieve the grey-scale image that better represents the original RGB image. The implemented algorithm has been validated on five public databases obtaining promising results. The average values obtained (a Jaccard s and Dice s coefficients of 0.8200 and 0.8932, respectively, an accuracy of 0.9947, and a true positive and false positive fractions of 0.9275 and 0.0036) demonstrate that this method is a robust tool for the automatic segmentation of the optic disc. Moreover, it is fairly reliable since it works properly on databases with a large degree of variability and improves the results of other state-of-the-art methods., This work was supported in part by the project IMIDTA/2010/47 and in part by projects Consolider-C (SEJ2006-14301/PSIC), "CIBER of Physiopathology of Obesity and Nutrition, an initiative of ISCIII" and Excellence Research Program PROMETEO (Generalitat Valenciana. Conselleria de Educacion, 2008-157).