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5 results on '"Université Toulouse 1 Capitole - UT1 (FRANCE)"'

1. An Axially Variant Kernel Imaging Model Applied to Ultrasound Image Reconstruction

Author

Sergiy A. Vorobyov, Denis Kouame, Mihai I. Florea, Adrian Basarab, Aalto University, CoMputational imagINg anD viSion (IRIT-MINDS), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Université Toulouse III - Paul Sabatier (UT3), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Aalto University (FINLAND), Institut de Recherche en Informatique de Toulouse - IRIT (Toulouse, France), and Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)

Subjects
Signal Processing (eess.SP), Image formation, Forward model, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Deconvolution, 02 engineering and technology, 01 natural sciences, Imaging, Convolution, Axially varying, Matrix-free, Ultrasound, Point-spread function, 0103 physical sciences, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, Boundary value problem, Electrical Engineering and Systems Science - Signal Processing, Electrical and Electronic Engineering, Invariant (mathematics), 010301 acoustics, Circulant matrix, ta213, Ultrasonic imaging, Applied Mathematics, 92C55, Computational modeling, Inverse problem, Modélisation et simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Kernel, Kernel (image processing), Image reconstruction, Signal Processing, 020201 artificial intelligence & image processing, Algorithm
Abstract

International audience; Existing ultrasound deconvolution approaches unrealistically assume, primarily for computational reasons, that the convolution model relies on a spatially invariant kernel and circulant boundary conditions. We discard both restrictions and introduce an image formation model applicable to ultrasound imaging and deconvolution based on an axially varying kernel, which accounts for arbitrary boundary conditions. Our model has the same computational complexity as the one employing spatially invariant convolution and has negligible memory requirements. To accommodate the state-of-the-art deconvolution approaches when applied to a variety of inverse problem formulations, we also provide an equally efficient adjoint expression for our model. Simulation results confirm the tractability of our model for the deconvolution of large images. Moreover, in terms of accuracy metrics, the quality of reconstruction using our model is superior to that obtained using spatially invariant convolution.

Published
2018

2. Skull Conductivity Estimation for EEG Source Localization

Author

Thomas Oberlin, Jean-Yves Tourneret, Facundo Costa, Hadj Batatia, Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Traitement et Compréhension d’Images (IRIT-TCI), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Signal et Communications (IRIT-SC), CoMputational imagINg anD viSion (IRIT-MINDS), and Institut National Polytechnique (Toulouse) (Toulouse INP)

Subjects
Computer science, Speech recognition, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Algorithme et structure de données, [SDV.BC]Life Sciences [q-bio]/Cellular Biology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Electroencephalography, Skull conductivity, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Matrix (mathematics), 0302 clinical medicine, M/EEG measurements, medicine, Electrical and Electronic Engineering, Eeg source localization, medicine.diagnostic_test, business.industry, Applied Mathematics, Pattern recognition, Magnetoencephalography, Bayes methods, Sparsity, Signal Processing, Biologie cellulaire, Artificial intelligence, Réseau de neurones, business, Source localization, 030217 neurology & neurosurgery
Abstract

International audience; A reliable leadfield matrix is needed to solve the magnetoencephalography/electroencephalography (M/EEG) source localization problem. The computation of this matrix requires sev- eral physical parameters, including the conductivity of the tissues that compose the subject’s head. Since it is not precisely known, we modify a recent Bayesian algorithm to estimate the skull conductivity jointly with the brain activity directly from the M/EEG measurements. Synthetic and real data are used to compare our technique with two optimization algorithms, showing that the pro- posed method is able to provide results of similar or better quality with the advantage of being applicable in a more general case.

Published
2017

3. The adaptive coherence estimator is the generalized likelihood ratio test for a class of heterogeneous environments

Author

Stephanie Bidon, Olivier Besson, Jean-Yves Tourneret, Université Toulouse 1 Capitole - UT1 (FRANCE), Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Centre National de la Recherche Scientifique - CNRS (FRANCE), and Université Toulouse - Jean Jaurès - UT2J (FRANCE)

Subjects
Wishart distribution, Generalized likelihood ratio test, Adaptive coherence estimator, Covariance matrix, Applied Mathematics, Estimator, Constant false alarm rate, Autre, Likelihood-ratio test, Signal Processing, Statistics, Prior probability, Adaptive detection, Nonhomogeneous environments, Detection theory, Electrical and Electronic Engineering, Random matrix, Algorithm, Mathematics
Abstract

The adaptive coherence estimator (ACE) is known to be the generalized likelihood ratio test (GLRT) in partially homogeneous environments, i.e., when the covariance matrix Ms of the secondary data is proportional to the covariance matrix Mp of the vector under test (or Ms = gamma/Mp). In this letter, we show that ACE is indeed the GLRT for a broader class of nonhomogeneous environments, more precisely when Ms is a random matrix, with inverse complex Wishart prior distribution whose mean only is proportional to Mp. Furthermore, we prove that, for this class of heterogeneous environments, the ACE detector satisfies the constant false alarm rate (CFAR) property with respect to gamma and Mp.

Published
2008

4. Knowledge-aided bayesian detection in heterogeneous environments

Author

Olivier Besson, Jean-Yves Tourneret, Stephanie Bidon, Université Toulouse 1 Capitole - UT1 (FRANCE), Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Centre National de la Recherche Scientifique - CNRS (FRANCE), and Université Toulouse - Jean Jaurès - UT2J (FRANCE)

Subjects
Covariance mapping, Covariance function, Computer science, Maximum likelihood, MathematicsofComputing_NUMERICALANALYSIS, Covariance intersection, Knowledge-aided processing, Heterogenous environments, Estimation of covariance matrices, Matrix (mathematics), Maximum a posteriori estimation, Rational quadratic covariance function, Traitement du signal et de l'image, Detection theory, Electrical and Electronic Engineering, CMA-ES, Bayesian detection, business.industry, Covariance matrix, Applied Mathematics, Matched filter, Detector, Pattern recognition, Covariance, Sample mean and sample covariance, Matérn covariance function, Signal Processing, Artificial intelligence, business
Abstract

We address the problem of detecting a signal of interest in the presence of noise with unknown covariance matrix, using a set of training samples. We consider a situation where the environment is not homogeneous, i.e., when the covariance matrices of the primary and the secondary data are different. A knowledge-aided Bayesian framework is proposed, where these covariance matrices are considered as random, and some information about the covariance matrix of the training samples is available. Within this framework, the maximum a priori (MAP) estimate of the primary data covariance matrix is derived. It is shown that it amounts to colored loading of the sample covariance matrix of the secondary data. The MAP estimate is in turn used to yield a Bayesian version of the adaptive matched filter. Numerical simulations illustrate the performance of this detector, and compare it with the conventional adaptive matched filter.

Published
2007

5. Negative Binomial Matrix Factorization

Author

Thomas Oberlin, Olivier Gouvert, Cédric Févotte, Signal et Communications (IRIT-SC), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS), ANR-19-P3IA-0004,ANITI,Artificial and Natural Intelligence Toulouse Institute(2019), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), and Université Toulouse 1 Capitole - UT1 (FRANCE)

Subjects
Poisson factorization, Applied Mathematics, Multiplicative function, Collaborative filtering, Negative binomial distribution, Informatique et langage, 020206 networking & telecommunications, Non-negative matrix factorization, 02 engineering and technology, Over-dispersion, Matrix decomposition, Matrix (mathematics), Overdispersion, Factorization, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, Applied mathematics, Majorization-minimization, Electrical and Electronic Engineering, Mathematics, Count data
Abstract

International audience; We introduce negative binomial matrix factoriza-tion (NBMF), a matrix factorization technique specially designed for analyzing over-dispersed count data. It can be viewed as an extension of Poisson factorization (PF) perturbed by a multiplicative term which models exposure. This term brings a degree of freedom for controlling the dispersion, making NBMF more robust to outliers. We describe a majorization-minimization (MM) algorithm for a maximum likelihood estimation of the parameters. We provide results on a recommendation task and demonstrate the ability of NBMF to efficiently exploit raw data.

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