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Academic literature on the topic 'Vues éparses'
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Journal articles on the topic "Vues éparses"
Foliard, Daniel. "La photographie comme absence : histoires en creux (Afrique, fin xixe-début xxe siècle)." Sources 6 (2023): 41–60. http://dx.doi.org/10.4000/11tb7.
Full textSchlosser, Jacques. "La Figure de Dieu Selon L'epitre Aux Philippiens." New Testament Studies 41, no. 3 (July 1995): 378–99. http://dx.doi.org/10.1017/s0028688500021548.
Full textVan Hoorde, Noëllie. "Les fictions jurisprudentielles en droit administratif : une dénégation de l’évidence." Civitas Europa N° 51, no. 2 (June 14, 2024): 87–100. http://dx.doi.org/10.3917/civit.051.0087.
Full textSmits, Jan. "Dutch Report: Coherence and Fragmentation of Private Law." European Review of Private Law 20, Issue 1 (February 1, 2012): 153–67. http://dx.doi.org/10.54648/erpl2012008.
Full textDissertations / Theses on the topic "Vues éparses"
Braure, Thomas. "Reconstruction tomographique multi-matériaux par vues éparses issues de sources impulsionnelles de haute énergie." Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPASP025.
Full textIn this doctoral thesis, we aim to design and evaluate for multi-material Computed Tomography (CT) method for physic experiments involving high-density objects subjected to extreme hydrodynamic stresses over a narrow time window. Under these conditions, radiographic acquisition requires the use of a limited number of high-energy pulsed X-ray sources, with varying operating characteristics, inducing significant degradation of experimental observations. Multimaterial CT estimation implies solving an ill-posed inverse problem with two uncertainties : the sparse-view geometry on the one hand, and material decomposition on the other. This inversion problem first requires the definition of a forward operator describing the physics of acquisition. Consequently, a model of a radiographic chain with a pulsed source is formulated. We propose an implementation supporting automatic differentiation and graphic acceleration, enabling rapid simulation of X-ray images and calculation of the error gradient with respect to experimental observations. This implementation produces results close to those obtained bytime-consuming and non-differentiable software. The bias induced by the simulations is estimated on the basis of observations acquired during experiments conducted on a standard mock-up. A preprocessing is also designed to correct additional experimental defects omitted by our model. The precise characterization of the forward operator allows us to consider the development of a reconstruction method. Thus, we propose a strategy based on unsupervised deep learning, independent of the experimental context and relying on latent optimization conditioning. This approach to CT works without prior learning by reparameterizing the reconstruction objective but can also benefit from initialization on a small training dataset to improve performance. Our method is evaluated with respect to the two uncertainties involved in the inversion problem. First, it is compared to state-of-the-art sparse-view CT models on open medical datasets for varying amounts of training data and radiographic projections. This study demonstrates that our strategy is parcimonious regarding these two factors, and tends to preserve the structural integrity of the reconstructions in these configurations. Then, multi-material CT of the standard mock-up are estimated, from simulation data using our implementation for several levels of degradation and from experimental images. Analysis of the results shows that material decomposition can be learned prior to experiments, and allows us to discuss the impact of simulation bias and degradation on the quality of the reconstructions
Books on the topic "Vues éparses"
Pierre, Legendre. Vues éparses: Entretiens radiophoniques avec Philippe Petit. [Paris]: Mille et une nuits, 2009.
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