Nicolas Gac

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—Back-Projection (BP) is a costly computational step in tomography image reconstruction such as Positron Emission Tomography (PET). To reduce the computation time, this paper presents a Pipelined, Pre-fetch and Par-allelized Architecture for PET BP (3PA-PET). The key feature of this architecture is its original memory access strategy, masking the high(More)
The reduction of image reconstruction time is needed to spread the use of PET for research and routine clinical practice. In this purpose, this article presents a hardware/software architecture for the acceleration of 3D backprojection based upon an efficient 2D backprojection. This architecture has been designed in order to provide a high level of(More)
Acoustic imaging is a standard technique for mapping acoustic source powers and positions from limited observations on microphone sensors, which often causes an ill-conditioned inverse problem. In this article, we firstly improve the forward model of acoustic power propagation by considering background noises at the sensor array, and the propagation(More)
In order to improve quality of 3D X-ray tomography reconstruction for Non Destructive Testing (NDT), we investigate in this paper hierarchical Bayesian methods. In NDT, useful prior information on the volume like the limited number of materials or the presence of homogeneous area can be included in the iterative reconstruction algorithms. In hierarchical(More)
A great number of image reconstruction algorithms, based on analytical filtered backprojection, are implemented for X-ray Computed Tomography (CT) [1, 3]. The limits of these methods appear when the number of projections is small, and/or not equidistributed around the object. In this specific context, iterative algebraic methods are implemented. A great(More)
– Dans cet article, nous nous sommes intéressés aux problèmes de reconnaissance non-coopérative de cibles en tant que problème de classification supervisée. Nous utilisons pour cela un algorithme des KPPV dont les performances sont détaillées en fonction du nombre de voisins K, du type de distance utilisé et de l'espace de représentation des données. Dans(More)
— La nécessité de définir des in-frastructures matérielles capables de supporter la grande diversité et complexité des algorithmes de traitement d'image, pour des tâches telles que la vision, se fait tou-jours plus forte. Aussi nous proposons une hiérachie mémoire bidimensionnelle susceptible de compenser, pour une large classe d'applications, la latence de(More)
The third edition of the " international-Traveling Workshop on Interactions between Sparse models and Technology " (iTWIST) will take place in Aalborg, the 4th largest city in Denmark situated beautifully in the northern part of the country. The workshop venue will be at the Aalborg University campus. One implicit objective of this biennial workshop is to(More)
In order to improve the quality of X-ray Computed Tomography (CT) reconstruction for Non Destructive Testing (NDT), we propose a hierarchical prior modeling with a Bayesian approach. In this paper we present a new hierarchical structure for the inverse problem of CT by using a multivariate Student-t prior which enforces sparsity and preserves edges. This(More)
Acoustic imaging is an advanced technique for acoustic source localization and power reconstruction from limited noisy measurements at microphone sensors. To solve this ill-posed inverse problem, the Bayesian inference methods using proper prior knowledge have been widely investigated. In this paper, we propose to use a hierarchical Variational Bayesian(More)