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Papers by Ali Mohammad-djafari

Research paper thumbnail of An alternative inference tool to total probability formula and its applications

AIP Conference Proceedings, 2004

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Research paper thumbnail of Regularization, Bayesian Inference and Machine Learning methods for Inverse Problems†

Classical methods for inverse problems are mainly based on regularization theory. In particular t... more Classical methods for inverse problems are mainly based on regularization theory. In particular those which are based on optimization of a criterion with two parts: a data-model matching and a regularization term. Different choices for these two terms and great number of optimization algorithms have been proposed. When these two terms are distance or divergence measures, they can have a Bayesian Maximum A Posteriori (MAP) interpretation where these two terms correspond, respectively, to the likelihood and prior probability models.

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Research paper thumbnail of Inverse problems in imaging science: from classical regularization methods to state of the art Bayesian methods

International Image Processing, Applications and Systems Conference, 2014

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Research paper thumbnail of Information, Entropy and Their Geometric Structures

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Research paper thumbnail of Application Of A Bayesian Inference Method To Reconstruct Short-Range Atmospheric Dispersion Events

Nucleation and Atmospheric Aerosols, 2011

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Research paper thumbnail of Information Physics: The New Frontier

Nucleation and Atmospheric Aerosols, 2011

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Research paper thumbnail of Problèmes inverses en imagerie et en vision en deux volumes inséparables (Traité Signal et Image, IC2)

HAL (Le Centre pour la Communication Scientifique Directe), 2009

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Research paper thumbnail of Imagerie micro−onde et application à la détection d'objets enfouis

HAL (Le Centre pour la Communication Scientifique Directe), Feb 1, 2009

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Research paper thumbnail of Information Theory of Quantum Systems with some hydrogenic applications

Nucleation and Atmospheric Aerosols, 2011

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Research paper thumbnail of A Method for Evaluating Tuning Functions of Single Neurons based on Mutual Information Maximization

Nucleation and Atmospheric Aerosols, 2011

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Research paper thumbnail of Fusion de données gammagraphiques et ultrasonores

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Research paper thumbnail of A Bayesian approach to Fourier Synthesis inverse problem with application in SAR imaging

Nucleation and Atmospheric Aerosols, 2011

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Research paper thumbnail of Inversion of large-support ill-conditioned linear operators using a Markov model with a line process

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Research paper thumbnail of Bayesian Modeling of a Human MMORPG Player

Nucleation and Atmospheric Aerosols, 2011

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Research paper thumbnail of A full-spectral Bayesian reconstruction approach based on the material decomposition model applied in dual-energy computed tomography

Medical Physics, Oct 22, 2013

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Research paper thumbnail of Maximum entropy and Bayesian methods : Paris, France, 1992 : proceedings of the twelfth International Workshop on Maximum Entropy and Bayesian Methods

Kluwer Academic eBooks, 1993

Preface. 1. Bayesian Inference and Maximum Entropy. 2. Quantum Physics and Quantum Information. 3... more Preface. 1. Bayesian Inference and Maximum Entropy. 2. Quantum Physics and Quantum Information. 3. Time Series. 4. Inverse Problems. 5. Applications. 6. Image Restoration and Reconstruction. Key Words Index. Authors' Index.

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Research paper thumbnail of Inverse problems in imaging systems and the general Bayesian inversion frawework

arXiv (Cornell University), May 18, 2007

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Research paper thumbnail of Une approche bayésienne de l'inversion en imagerie micro-onde 3D

HAL (Le Centre pour la Communication Scientifique Directe), Mar 19, 2008

Le probleme de la tomographie 3D est modelise par deux equations integrales couplees qui exprimen... more Le probleme de la tomographie 3D est modelise par deux equations integrales couplees qui expriment les champs electrique observe et existant a l'interieur de l'objet a imager. La discretisation de ces deux equations par une methode des moments nous ramene a un jeux de deux equations algebriques matricielles avec deux inconnues qui sont le contraste de l'objet et le champs total a l'interieur de l'objet. Ces deux equations sont de tres grandes dimensions. Par ailleurs, il y trois sources d'erreurs : i) le bruit de mesure proprement dit, ii) l'erreur de discretisation de l'objet, et ii) l'erreur liee aux approximations dans le calcul des elements des deux matrices. L'idee dans les approches probabilistes est de modeliser ces erreurs pour les prendre en compte dans le calcul de la solution. De plus, l'approche bayesienne nous permet aussi de prendre en compte l'information a priori sur les inconnues du probleme. Dans les applications en CND, souvent l'objet etudie est compose d'un nombre fini de materiaux, ce qui implique que l'image recherchee est constituee d'un nombre fini de regions homogenes et compactes, ce qui justifie la modelisation de sa distribution par une melange de gaussiennes avec une variable cachee representant l'etiquette des regions. Nous avons deja utilise cette approche en 2D avec succes et l'objet de cette these est l'extension en 3D. Dans cette expose nous presentons surtout les difficultes que l'on peut rencontrer du point de vue du calcul, et les idees que l'on propose pour la solution.

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Research paper thumbnail of Séparation d'images

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Research paper thumbnail of Estimation des hyperparamètres dans une approche bayésienne de la résolution des problèmes inverses linéaires

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Research paper thumbnail of An alternative inference tool to total probability formula and its applications

AIP Conference Proceedings, 2004

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Research paper thumbnail of Regularization, Bayesian Inference and Machine Learning methods for Inverse Problems†

Classical methods for inverse problems are mainly based on regularization theory. In particular t... more Classical methods for inverse problems are mainly based on regularization theory. In particular those which are based on optimization of a criterion with two parts: a data-model matching and a regularization term. Different choices for these two terms and great number of optimization algorithms have been proposed. When these two terms are distance or divergence measures, they can have a Bayesian Maximum A Posteriori (MAP) interpretation where these two terms correspond, respectively, to the likelihood and prior probability models.

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Research paper thumbnail of Inverse problems in imaging science: from classical regularization methods to state of the art Bayesian methods

International Image Processing, Applications and Systems Conference, 2014

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Research paper thumbnail of Information, Entropy and Their Geometric Structures

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Research paper thumbnail of Application Of A Bayesian Inference Method To Reconstruct Short-Range Atmospheric Dispersion Events

Nucleation and Atmospheric Aerosols, 2011

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Research paper thumbnail of Information Physics: The New Frontier

Nucleation and Atmospheric Aerosols, 2011

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Research paper thumbnail of Problèmes inverses en imagerie et en vision en deux volumes inséparables (Traité Signal et Image, IC2)

HAL (Le Centre pour la Communication Scientifique Directe), 2009

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Imagerie micro−onde et application à la détection d'objets enfouis

HAL (Le Centre pour la Communication Scientifique Directe), Feb 1, 2009

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Information Theory of Quantum Systems with some hydrogenic applications

Nucleation and Atmospheric Aerosols, 2011

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Method for Evaluating Tuning Functions of Single Neurons based on Mutual Information Maximization

Nucleation and Atmospheric Aerosols, 2011

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Research paper thumbnail of Fusion de données gammagraphiques et ultrasonores

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Bayesian approach to Fourier Synthesis inverse problem with application in SAR imaging

Nucleation and Atmospheric Aerosols, 2011

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Inversion of large-support ill-conditioned linear operators using a Markov model with a line process

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Bayesian Modeling of a Human MMORPG Player

Nucleation and Atmospheric Aerosols, 2011

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A full-spectral Bayesian reconstruction approach based on the material decomposition model applied in dual-energy computed tomography

Medical Physics, Oct 22, 2013

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Research paper thumbnail of Maximum entropy and Bayesian methods : Paris, France, 1992 : proceedings of the twelfth International Workshop on Maximum Entropy and Bayesian Methods

Kluwer Academic eBooks, 1993

Preface. 1. Bayesian Inference and Maximum Entropy. 2. Quantum Physics and Quantum Information. 3... more Preface. 1. Bayesian Inference and Maximum Entropy. 2. Quantum Physics and Quantum Information. 3. Time Series. 4. Inverse Problems. 5. Applications. 6. Image Restoration and Reconstruction. Key Words Index. Authors' Index.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Inverse problems in imaging systems and the general Bayesian inversion frawework

arXiv (Cornell University), May 18, 2007

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Research paper thumbnail of Une approche bayésienne de l'inversion en imagerie micro-onde 3D

HAL (Le Centre pour la Communication Scientifique Directe), Mar 19, 2008

Le probleme de la tomographie 3D est modelise par deux equations integrales couplees qui exprimen... more Le probleme de la tomographie 3D est modelise par deux equations integrales couplees qui expriment les champs electrique observe et existant a l'interieur de l'objet a imager. La discretisation de ces deux equations par une methode des moments nous ramene a un jeux de deux equations algebriques matricielles avec deux inconnues qui sont le contraste de l'objet et le champs total a l'interieur de l'objet. Ces deux equations sont de tres grandes dimensions. Par ailleurs, il y trois sources d'erreurs : i) le bruit de mesure proprement dit, ii) l'erreur de discretisation de l'objet, et ii) l'erreur liee aux approximations dans le calcul des elements des deux matrices. L'idee dans les approches probabilistes est de modeliser ces erreurs pour les prendre en compte dans le calcul de la solution. De plus, l'approche bayesienne nous permet aussi de prendre en compte l'information a priori sur les inconnues du probleme. Dans les applications en CND, souvent l'objet etudie est compose d'un nombre fini de materiaux, ce qui implique que l'image recherchee est constituee d'un nombre fini de regions homogenes et compactes, ce qui justifie la modelisation de sa distribution par une melange de gaussiennes avec une variable cachee representant l'etiquette des regions. Nous avons deja utilise cette approche en 2D avec succes et l'objet de cette these est l'extension en 3D. Dans cette expose nous presentons surtout les difficultes que l'on peut rencontrer du point de vue du calcul, et les idees que l'on propose pour la solution.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Séparation d'images

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Estimation des hyperparamètres dans une approche bayésienne de la résolution des problèmes inverses linéaires

Bookmarks Related papers MentionsView impact