Montbrison – Sainte-Eugénie

En 1992, s’est achevée la phase d’étude préliminaire du site du Clos Sainte-Eugénie. Cette opération, d’une durée de deux ans, fait suite à l’acquisition et au classement au titre des monuments historiques des vestiges des thermes antiques d’Aquae Segetae. Il s’agissait, par cette étude, d’évaluer les potentialités du site, l’organisation des vestiges et leur extension, et d’en préciser la chronologie, afin de permettre aux partenaires concernés par son devenir de disposer des éléments néces..

ICA-based sparse feature recovery from fMRI datasets

Spatial Independent Components Analysis (ICA) is increasingly used in the context of functional Magnetic Resonance Imaging (fMRI) to study cognition and brain pathologies. Salient features present in some of the extracted Independent Components (ICs) can be interpreted as brain networks, but the segmentation of the corresponding regions from ICs is still ill-controlled. Here we propose a new ICA-based procedure for extraction of sparse features from fMRI datasets. Specifically, we introduce a new thresholding procedure that controls the deviation from isotropy in the ICA mixing model. Unlike current heuristics, our procedure guarantees an exact, possibly conservative, level of specificity in feature detection. We evaluate the sensitivity and specificity of the method on synthetic and fMRI data and show that it outperforms state-of-the-art approaches

Rhéologie des boues de stations d'épuration : études préliminaires pour la maîtrise des stockages et épandages

Les boues résiduaires pâteuses sont des matériaux au comportement complexe. Trop fluides pour tenir sous forme de tas, elles sont néanmoins trop solides pour être épandues correctement avec les moyens mécaniques actuellement utilisés en agriculture. Aussi, afin de réduire ces deux principales entraves à la valorisation agricole, et hormis les différentes réticences d'ordre psychologique, une étude du comportement mécanique de ces boues a été menée récemment. Dans cette optique, et après avoir décrit les filières de traitement des boues, nous nous sommes attachés à mettre en évidence les caractéristiques jugées pertinentes. Des parallèles ont été établis dans ce but et dans plusieurs domaines avec d'autres matériaux, tout en gardant à l'esprit les spécificités de la boue. Enfin, dans le cadre de la mise au point de notre démarche expérimentale, nous avons illustré notre propos par un cas concret. / Soft residual sewage sludge behaves in a complex way. It is too liquid to form into a heap but too solid to be spread properly using current machinery. A study of the mechanical behaviour of this sludge has been undertaken recently to remove these two major obstacles to agricultural recycling, other than various psychological reservations. After a preliminary description of sewerage treatment processes, we have attempted to highlight the characteristics considered to be the most relevant. Parallels are drawn with other materials in several fields, bearing in mind the specific characteristics of sludge. Finally, in order to finalise our experimental approach, we illustrate our proposal with a specific case

Improving accuracy and power with transfer learning using a meta-analytic database

Typical cohorts in brain imaging studies are not large enough for systematic testing of all the information contained in the images. To build testable working hypotheses, investigators thus rely on analysis of previous work, sometimes formalized in a so-called meta-analysis. In brain imaging, this approach underlies the specification of regions of interest (ROIs) that are usually selected on the basis of the coordinates of previously detected effects. In this paper, we propose to use a database of images, rather than coordinates, and frame the problem as transfer learning: learning a discriminant model on a reference task to apply it to a different but related new task. To facilitate statistical analysis of small cohorts, we use a sparse discriminant model that selects predictive voxels on the reference task and thus provides a principled procedure to define ROIs. The benefits of our approach are twofold. First it uses the reference database for prediction, i.e. to provide potential biomarkers in a clinical setting. Second it increases statistical power on the new task. We demonstrate on a set of 18 pairs of functional MRI experimental conditions that our approach gives good prediction. In addition, on a specific transfer situation involving different scanners at different locations, we show that voxel selection based on transfer learning leads to higher detection power on small cohorts.Comment: MICCAI, Nice : France (2012

New SAR Target Imaging Algorithm based on Oblique Projection for Clutter Reduction

International audienceWe have developed a new Synthetic Aperture Radar (SAR) algorithm based on physical models for the detection of a Man-Made Target (MMT) embedded in strong clutter (trunks in a forest). The physical models for the MMT and the clutter are represented by low-rank subspaces and are based on scattering and polarimetric properties. Our SAR algorithm applies the oblique projection of the received signal along the clutter subspace onto the target subspace. We compute its statistical performance in terms of probabilities of detection and false alarms. The performances of the proposed SAR algorithm are improved compared to those obtained with existing SAR algorithms: the MMT detection is greatly improved and the clutter is rejected. We also studied the robustness of our new SAR algorithm to interference modeling errors. Results on real FoPen (Foliage Penetration) data showed the usefulness of this approach

A geometric alternative to computed tomography

This paper describes parts of a patent taken out by the INRIAThis paper describes a totally new way to process Xray data in order to reconstruct the external and internal boudaries of objects, which do not involve Computed Tomography (CT). We show that the segmentation can be performed directrly with the raw data, the sinogram produced with the scanner, and that those segmented shapes can be geomtetrically transformed into reconstructed shapes in the usual space. Thus, if we are interested in only the boundaries of the objects, our method eliminates the computationally expensive step of Computed Tomography. Experimental results are presented for both synthetic and real data, leading to subpixel positioning fo the reconstructed boundaries. Our method gives its best results for sparse, high contrasted objects such as bones or blood vessels in angiograms. It can be adapted to any kind of scanner, including 3D scabbers. At last, we present an extension of our method which allows "on the fly" processing of the data and real time tracking of the objects boudnaries

New feature points based on geometric invariants for 3D image registration

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The Gradient and laplacien filtered back projection operators

This paper presents a new method, based on the Filtered Back Projection technique (FBP) to compute directly the values of the gradient and the laplacien of an X-Ray image. We propose a way to pre-process the raw data that allow us to compute directly the reconstructed values of the gradient or of the laplacien at any location in the plane (defined with real coordinates) without reconstructing an image of the absorption coefficients. The reconstructed values of the gradient and of the laplacien correspond to the exact mathematical definition of the differentials of the image and do not imply the use of a band limited filter depending on a constant s, as proposed previously by other authors. For noisy data, we propose also an extension of existing FBP techniques, adapted to the computation of the gradient and of the laplacien. At last, we show how to use those new algorithms to perform the segmentation of a slice, without image reconstruction. Images of the reconstructed gradient, laplacien and segmented objects are presented

Realistic 3D simulation of shapes and shadows for image processing

This paper illustrates the cooperation between Image Processing and Computer Graphics. We present a new method to compute realistic 3D images of building or complex objects from a set of real pictures and from the 3D model of a real scene. We also show how to remove shadows from those pictures and how to simulate new lightnings. Our system allows the generation of synthetic pictures, with a total control over the position of the camera, over the features of the optical system, and over the solar lightning. We propose several methods to avoid most of the artefact which would be produced by a straghtforward application of our approach. At last, we propose a general scheme to use these pictures in order to design new optical systems and to test Image Processing algorithms, long before the building of the first physical prototype

Machine Learning for Neuroimaging with Scikit-Learn

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g. resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.Comment: Frontiers in neuroscience, Frontiers Research Foundation, 2013, pp.1