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Nicolas Dobigeon
187
Documents
Presentation
Nicolas Dobigeon a reçu le Diplôme d'Ingénieur en Électronique de l'[ENSEEIHT](http://www.enseeiht.fr/) et le D.E.A. (Master Recherche) en Traitement du Signal de l'[Institut National Polytechnique de Toulouse](http://www.inp-toulouse.fr/) (INP Toulouse) en Juin 2004. En 2007, il a obtenu un Doctorat en Traitement du Signal de l'INP de Toulouse et, en 2012, il a reçu l'Habilitation à Diriger des Recherches (HDR) de l'INP de Toulouse. De 2007 à 2008, il a été chercheur post-doctoral associé au Département Electrical Engineering and Computer Science de l'[Université du Michigan](http://www.umich.edu/) à Ann Arbor.
En 2008, il a été nommé à l'Institut National Polytechnique de Toulouse ([Université de Toulouse](http://www.univ-toulouse.fr/), INP-ENSEEIHT) où il est actuellement Professeur. Il mène ses activités de recherche au sein de l'équipe [Signal et Communications](http://sc.enseeiht.fr/) du laboratoire [IRIT](http://www.irit.fr/) (UMR CNRS 5505) et et est aussi membre associé de l'équipe-projet [Apprentissage Optimisation Complexité](https://perso.math.univ-toulouse.fr/aoc/) (AOC) du LabEx [CIMI](http://www.cimi.univ-toulouse.fr/fr). Il porte une Chaire à l'Institut Interdisciplinaire d'Intelligence Artificielle 3IA [ANITI](http://aniti.univ-toulouse.fr) (Artificial and Natural Intelligence Toulouse Institute) et est Membre Honoraire de l'[Institut Universitaire de France](http://www.iufrance.fr/) (IUF, Promotion Junior 2017).
Ses activités de recherche concernent principalement le traitement statistique du signal et des images, avec un intérêt particulier pour les problèmes inverses bayésiens rencontrés en télédétection, imagerie médicale et microscopie.
Il est actuellement Senior Area Editor pour [IEEE Trans. Signal Processing](https://signalprocessingsociety.org/publications-resources/ieee-transactions-signal-processing) (depuis 2022), responsable adjoint du Département [Signaux et Images](https://www.irit.fr/-Signaux-images,7-) de l'IRIT (depuis 2017), et membre du groupe d'animation de l'axe stratégique "[Numérique et Société](https://www.univ-toulouse.fr/recherche-dynamique/strategie-scientifique-partagee)" à l'Université Fédérale Toulouse Midi-Pyrénées (depuis 2020). Il est le responsable de la formation de spécialité "[Intelligence Artificielle pour le Traitement de l'Information](http://sc.enseeiht.fr/iati/index.html)" à l'INP-ENSEEIHT (depuis 2022).
Il était Éditeur Associé pour [IEEE Trans. Signal Processing](https://signalprocessingsociety.org/publications-resources/ieee-transactions-signal-processing) (2020-2022), [Signal Processing](https://www.journals.elsevier.com/signal-processing) (2016-2020) et [Digital Signal Processing](https://www.journals.elsevier.com/digital-signal-processing) (2018-2020), membre du comité technique [SPTM TC](https://signalprocessingsociety.org/get-involved/signal-processing-theory-and-methods) de IEEE Signal Processing Society (2018-2020), membre du Comité de Direction du [GDR-ISIS](http://gdr-isis.fr/) (2013-2018), membre nommé puis élu au [Conseil National des Universités](https://www.conseil-national-des-universites.fr/) (CNU-Section 61, 2012-2016) et élu au Conseil Scientifique de Toulouse INP (2014-2016). Il a également été en charge la formation de spécialité "[Traitement du Signal et des Images](http://sc.enseeiht.fr/tsi/)" au sein du Département Electronique de l'INP-ENSEEIHT (2011-2019).
Il est IEEE Senior Member et membre de European Laboratory for Learning and Intelligent Systems ([ELLIS](https://ellis.eu/)).
\----
Professional Webpage: [https://ndobigeon.github.io/](http://dobigeon.perso.enseeiht.fr/)
Google Scholar Profile: [https://scholar.google.com/citations?hl=en&user=YPQFwDYAAAAJ](https://scholar.google.com/citations?hl=en&user=YPQFwDYAAAAJ)
Nicolas Dobigeon received the Eng. degree in Electrical Engineering from [INP](http://www.inp-toulouse.fr/)-[ENSEEIHT](http://www.enseeiht.fr/), Toulouse, France, and the M.Sc. degree in Signal Processing from the [Institut National Polytechnique de Toulouse](http://www.inp-toulouse.fr/) (Toulouse INP), both in June 2004. He received the Ph.D. degree and Habilitation à Diriger les Recherches in Signal Processing from Toulouse INP in 2007 and 2012, respectively. From 2007 to 2008, he was a Postdoctoral Research Associate with the Department of Electrical Engineering and Computer Science, [University of Michigan](http://www.umich.edu/), Ann Arbor.
Since 2008, Nicolas Dobigeon has been with Toulouse INP (INP-ENSEEIHT, [University of Toulouse](https://en.univ-toulouse.fr/)) where he is currently a Professor. He conducts his research within the [Signal and Communications](http://sc.enseeiht.fr/) (SC) group of [IRIT](http://www.irit.fr) and is an associate member of the [Apprentissage Optimisation Complexité](https://perso.math.univ-toulouse.fr/aoc/) (AOC) project-team of [CIMI](http://www.cimi.univ-toulouse.fr/fr). He currently holds an AI Research Chair at the [Artificial and Natural Intelligence Toulouse Institute](http://aniti.univ-toulouse.fr/index.php/en/) (ANITI) and he is a Junior Member of the [Institut Universitaire de France](http://www.iufrance.fr/) (IUF, 2017-2022).
His recent research activities have been focused on statistical signal and image processing, with a particular interest in Bayesian inverse problems and applications to remote sensing, biomedical imaging and microscopy.
He is currently a Senior Area Editor for [IEEE Trans. Signal Processing](https://signalprocessingsociety.org/publications-resources/ieee-transactions-signal-processing) (since 2022), the Deputy Head of the Dept. of [Signals and Images](https://www.irit.fr/-Signaux-images,7-) at IRIT (since 2017) and a member of the scientific working group dedicated to "D[igital society](https://www.univ-toulouse.fr/recherche-dynamique/strategie-scientifique-partagee)" for the Fed. Univ. Toulouse Midi-Pyrénées (since 2020). He is the Head of the Major Program "[Artificial Intelligence for Information Processing](http://sc.enseeiht.fr/iati/index.html)" at INP-ENSEEIHT (since 2022).
He was an Associate Editor for [IEEE Trans. Signal Processing](https://signalprocessingsociety.org/publications-resources/ieee-transactions-signal-processing) (2020-2022), [Signal Processing](https://www.journals.elsevier.com/signal-processing) (2016-2020) and [Digital Signal Processing](https://www.journals.elsevier.com/digital-signal-processing) (2018-2020), a member of the [SPTM TC](https://signalprocessingsociety.org/get-involved/signal-processing-theory-and-methods) of the IEEE Signal Processing Society (2018-2020), a steering committee member of the French research group [GDR-ISIS](http://gdr-isis.fr/) (2013-2018), a member of [French National Council of Universities](https://www.conseil-national-des-universites.fr/) (CNU/Section 61, 2012-2016) and an elected member at the Research Council of Toulouse INP (2014-2016). He was the Head of the Major Program on "[Signal and Image Processing](http://sc.enseeiht.fr/tsi/)" at INP-ENSEEIHT (2011-2019).
He is an IEEE Senior Member and a Member of the European Laboratory for Learning and Intelligent Systems ([ELLIS](https://ellis.eu/)).
\----
Professional Webpage: [https://ndobigeon.github.io/](http://dobigeon.perso.enseeiht.fr/)
Google Scholar Profile: [https://scholar.google.com/citations?hl=en&user=YPQFwDYAAAAJ](https://scholar.google.com/citations?hl=en&user=YPQFwDYAAAAJ)
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Successive Nonnegative Projection Algorithm for Linear Quadratic Mixtures (EUSIPCO 2020)28th European Signal Processing Conference (EUSIPCO 2020), EURASIP : European Association for Signal Processing, Jan 2021, Amsterdam (virtual), Netherlands. pp.1951-1955, ⟨10.23919/Eusipco47968.2020.9287788⟩
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Successive Nonnegative Projection Algorithm for Linear Quadratic Mixtures (iTWIST 2020)International Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques (iTWIST 2020), Dec 2020, Nantes (virtual), France
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Unsupervised change detection for multimodal remote sensing images via coupled dictionary learning and sparse codingIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), May 2020, Barcelona, Spain. pp.4627-4631
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Fusion of hyperspectral and multispectral infrared astronomical images11th IEEE Workshop on Sensor Array and Multichannel Signal Processing (SAM 2020), IEEE, Jun 2020, Hangzhou, China. pp.1-5, ⟨10.1109/SAM48682.2020.9104393⟩
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Bayesian Image Restoration under Poisson Noise and Log-concave PriorICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2019, Brighton, United Kingdom. pp.1712-1716, ⟨10.1109/ICASSP.2019.8683031⟩
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Unmixing dynamic PET images: combining spatial heterogeneity and non-Gaussian noiseIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2019), May 2019, Brighton, United Kingdom. pp.1373-1377
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Modèles augmentés asymptotiquement exacts27e colloque du Groupe de Recherche et d'Etudes du Traitement du Signal et des Images (GRETSI 2019), Aug 2019, Lille, France. pp.1-4
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Reconstruction of partially sampled STEM-EELS images with atomic resolutionWorkshop on Signal Processing with Adaptative Sparse Structured Representations (SPARS 2019), Nicolas Dobigeon, Toulouse INP, France; Cédric Févotte, CNRS, France, Jul 2019, Toulouse, France
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On variable splitting for Markov chain Monte CarloWorkshop on Signal Processing with Adaptative Sparse Structured Representations (SPARS 2019), Apr 2019, Toulouse, France. pp.1-2
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Cofactorisation de matrices pour le démélange et la classification conjoints d'images hyperspectrales (SFPT-GH 2019)7e Colloque du groupe SFPT-GH : Groupe Hyperspectral de la Société Française de Photogrammétrie et de Télédétection (SFPT-GH 2019), Jul 2019, Toulouse, France. pp.1
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Matrix Cofactorization for Joint Unmixing and Classification of Hyperspectral Images27th European Signal Processing Conference (EUSIPCO 2019), Sep 2019, A Coruna, Spain. pp.1-5
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Reconstruction de spectres-images STEM-EELS partiellement échantillonnés16e Colloque de la Société Française des Microscopies (SFU 2019), Jul 2019, Poitiers, France. pp.74-75
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Efficient Sampling through Variable Splitting-inspired Bayesian Hierarchical ModelsICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Institute of Electrical and Electronics Engineers (IEEE), May 2019, Brighton, United Kingdom. pp.5037-5041, ⟨10.1109/ICASSP.2019.8682982⟩
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Fusion d'images multispectrales et hyperspectrales pour l'observation en astronomie infrarouge27e colloque du Groupe de Recherche et d'Etudes du Traitement du Signal et des Images (GRETSI 2019), Aug 2019, Lille, France. pp.1-4
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Modèle bayésien hiérarchique pour le démélange et la classification robuste d’images hyperspectralesConférence Française de Photogrammétrie et Télédétection 2018, Jun 2018, Marne la Vallée, France. 3 p
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Un modèle bayésien pour le démélange, la segmentation et la classification robuste d’images hyperspectrales26. Colloque GRETSI 2017, Sep 2017, Juan-Les-Pins, France
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Détection de changements par fusion robuste d'images multi-bandes de résolutions spatiale et spectrale différentes26eme Colloque GRETSI sur le Traitement du Signal et des Images (GRETSI 2017), Sep 2017, Juan-les-Pins, France. pp.1-4
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Une formulation bayésienne du codage antiparcimonieuxGRETSI, Sep 2017, Juan-les-Pins, France
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Fast hyperspectral unmixing in presence of sparse multiple scattering nonlinearities42nd IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2017), Mar 2017, New Orleans, United States. pp. 3111-3115
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Ecosystem services assessment using hyperspectral images5. Colloque de la Société Française de Photogrammétrie et Télédétection - Groupe Hyperspectrale (SFPT-GH), May 2017, Brest, France
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Unmixing multitemporal hyperspectral images accounting for smooth and abrupt variations25th European Signal Processing Conference (EUSIPCO 2017), Aug 2017, Kos island, Greece. pp. 1-5
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Change detection between multi-band images using a robust fusion-based approach42nd IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2017), Mar 2017, New Orleans, United States. pp. 3346-3350
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Bayesian Nonparametric Subspace EstimationICASSP 2017 - IEEE International Conference on Acoustics, Speech and Signal Processing, Mar 2017, New Orleans, United States. ⟨10.1109/ICASSP.2017.7952556⟩
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A Bayesian model for joint unmixing, clustering and classification of hyperspectral dataSéminaire des doctorants, Apr 2017, Toulouse, France. pp. 1-28
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Bayesian-driven criterion to automatically select the regularization parameter in the l1-Potts model42nd IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2017), Mar 2017, New Orleans, United States. pp. 1
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Unmixing dynamic PET images with a PALM algorithm25th European Signal Processing Conference (EUSIPCO 2017), Aug 2017, Kos island, Greece. pp. 1-5
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High-resolution Hyperspectral Image Fusion Based on Spectral Unmixing19th International Conference on Information Fusion (FUSION 2016), Jul 2016, Heidelberg, Germany. pp. 1714-1719
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Joint segmentation of multiple images with shared classes: a Bayesian nonparametrics approachIEEE Workshop on statistical signal processing (SSP 2016), Jun 2016, Palma de Mallorca, Spain. pp.1-5, ⟨10.1109/SSP.2016.7551735⟩
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Democratic prior for anti-sparse codingIEEE Workshop on statistical signal processing (SSP 2016), Jun 2016, Palma de Mallorca, Spain. pp. 1-5
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Unmixing multitemporal hyperspectral images with variability: an online algorithmIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2016), Mar 2016, Shangai, China. pp. 3351-3355
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Blind model-based fusion of multi-band and panchromatic imagesIEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016), Sep 2016, Baden-Baden, Germany. pp. 21-25
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Estimation bayésienne locale du paramètre de multifractalité à l'aide d'un algorithme de Monte Carlo Hamiltonien25eme Colloque Groupe de Recherche et d'Etudes du Traitement du Signal et des Images (GRETSI 2015), Sep 2015, Lyon, France. pp. 1-5
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A new Bayesian unmixing algorithm for hyperspectral images mitigating endmember variabilityICASSP 2015, 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, Apr 2015, Brisbane, Australia. pp.2469-2473, ⟨10.1109/ICASSP.2015.7178415⟩
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Estimation de variabilité pour le démélange non-supervisé d'images hyperspectrales25eme Colloque Groupe de Recherche et d'Etudes du Traitement du Signal et des Images (GRETSI 2015), Sep 2015, Lyon, France. pp. 1-4
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A perturbed linear mixing model accounting for spectral variability23rd European Signal and Image Processing Conference (EUSIPCO 2015), Aug 2015, Nice, France. pp. 814-818
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Hyperspectral unmixing accounting for spatial correlations and endmember variability7th IEEE Workshop on Hyperspectral Image and SIgnal Processing: Evolution in Remote Sensing (WHISPERS 2015), Jun 2015, Tokyo, Japan. pp. 1-4
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Unmixing multitemporal hyperspectral images accounting for endmember variability23rd European Signal and Image Processing Conference (EUSIPCO 2015), Aug 2015, Nice, France. pp. 1686-1690
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A Bayesian approach for the joint estimation of the multifractality parameter and integral scale based on the Whittle approximation40th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2015), Apr 2015, Brisbane, Australia. pp.3886-3890, ⟨10.1109/ICASSP.2015.7178699⟩
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Bayesian fusion of multispectral and hyperspectral images using a block coordinate descent methodIEEE Workshop on Hyperspectral Image and SIgnal Processing: Evolution in Remote Sensing (WHISPERS 2015), Jun 2015, Tokyo, Japan. pp. 1-5
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FUSE: a fast multi-band image fusion algorithm6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2015), Dec 2015, Cancun, Mexico. pp. 161-164
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Démélange non-linéaire d'images hyperspectrales : mythe ou réalité ?3ème colloque scientifique de la SFPT-GH - 2014, May 2014, Porquerolles, France
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Nonlinear unmixing of vegetated areas: a model comparison based on simulated and real hyperspectral dataIEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing - WHISPERS 2014, Jun 2014, Lausanne, Switzerland. pp. 1-4
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Bayesian fusion of multispectral and hyperspectral images with unknown sensor spectral responseIEEE International Conference on Image Processing (ICIP 2014), Oct 2014, Paris, France. pp. 698-702
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Bayesian fusion of hyperspectral and multispectral imagesIEEE International Conference on Acoustics, Speech, and Signal Processing - ICASSP 2014, May 2014, Florence, Italy. pp. 3176-3180
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Fusion of multispectral and hyperspectral images based on sparse representation22nd European Signal and Image Processing Conference (EUSIPCO 2014), Sep 2014, Lisbon, Portugal. pp. 1577-1581
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Learning a fast transform with a dictionaryiTwist, Aug 2014, Namur, Belgium. pp.10-12
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Inverse problem formulation for regularity estimation in imagesInternational Conference on Image Processing (ICIP 2014), Oct 2014, Paris, France. pp. 6081-6085
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A hierarchical sparsity-smoothness Bayesian model for ℓ0 + ℓ1 + ℓ2 regularizationIEEE International Conference on Acoustics, Speech, and Signal Processing - ICASSP 2014, May 2014, Florence, Italy. pp. 1901-1905
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Bayesian algorithm for unsupervised unmixing of hyperspectral images using a post-nonlinear model21st European Signal and Image Processing Conference (EUSIPCO 2013), Sep 2013, Marrakech, Morocco. pp. 1-5
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Estimation bayésienne du paramètre de multifractalité24eme Colloque Groupe de Recherche et d'Etudes du Traitement du Signal et des Images (GRETSI 2013), Sep 2013, Brest, France. pp. 1-4
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Bayesian estimation for the multifractality parameterIEEE International Conference on Acoustics, Speech, and Signal Processing - ICASSP 2013, May 2013, Vancouver, Canada. pp. 6556-6560
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Robust nonnegative matrix factorization for nonlinear unmixing of hyperspectral imagesIEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing - WHISPERS 2013, Jun 2013, Gainesville, United States. pp. 1-4
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Nonlinear hyperspectral unmixing using Gaussian processesIEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing - WHISPERS 2013, Jun 2013, Gainesville, United States. pp. 1-4
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A robust test for nonlinear mixture detection in hyperspectral imagesIEEE International Conference on Acoustics, Speech, and Signal Processing - ICASSP 2013, May 2013, Vancouver, Canada. pp. 2149-2153
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Regularized Bayesian compressed sensing in ultrasound imaging20th European Signal and Image Processing Conference (EUSIPCO 2012), European Association for Signal, Speech, and Image Processing (EURASIP), Aug 2012, Bucharest, Romania. pp.2600--2604
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Un modèle Bayésien de mélange de lois Poisson-Gamma pour segmenter des images TEPRFIA 2012 (Reconnaissance des Formes et Intelligence Artificielle), Jan 2012, Lyon, France. pp.978-2-9539515-2-3
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Accuracy and performance of linear unmixing techniques for detecting minerals on OMEGA/MExEPSC-DPS Joint Meeting, Oct 2011, Nantes, France. pp.969
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Variational methods for spectral unmixing of hyperspectral images2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2011, Prague, Czech Republic. pp.957-960, ⟨10.1109/ICASSP.2011.5946564⟩
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Echantillonnage compressé Bayésien en imagerie ultrasonoreColloque du Groupe de Recherche et d'Etudes du Traitement du Signal et des Images (GRETSI 2011), Sep 2011, Bordeaux, France. (support électronique)
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Bayesian compressed sensing in ultrasound imaging4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2011), Sensor Array and Multi-channel Technical Committee of the IEEE Signal Processing Society, Dec 2011, San Juan, PR, United States. pp.101--104, ⟨10.1109/CAMSAP.2011.6135897⟩
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Multi-image unsupervised spectral analysisEuropean Planetary Science Congress, Sep 2010, Rome, Italy. pp.8
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Applying new spectral unmixing technique to Martian hyperspectral dataEGU General Assembly, May 2010, Vienne, Austria. pp.12824
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Investigating Martian and Venusian hyperspectral datasetsAGU Fall Meeting, Dec 2010, San Francisco, United States. pp.B1518
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Accuracy and performance of optimized Bayesian source separation for hyperspectral unmixingWhispers'10, Jun 2010, Reykjavik, Iceland. pp.24
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Matrix approximation techniques for unsupervised hyperspectral data analysisEGU General Assembly, May 2010, Vienne, Austria. pp.10027
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Extraction de composants purs et démélange linéaire bayésiens en imagerie hyperspectraleXIIème colloque GRETSI sur le Traitement du Signal et des Images, Sep 2009, Dijon, France. pp.CDROM
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Material identification on martian hyperspectral images using bayesian source separationFirst IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Aug 2009, Grenoble, France. pp.1-4, ⟨10.1109/WHISPERS.2009.5289091⟩
Conference papers
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Subspace-based Bayesian blind source separation for hyperspectral imagery3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2009), Dec 2009, Aruba, Antilles, Netherlands. pp.372-375, ⟨10.1109/CAMSAP.2009.5413255⟩
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Blind unmixing of linear mixtures using a hierarchical Bayesian model. Application to spectroscopic signal analysisIEEE Workshop on Statistical Signal Processing, Aug 2007, Madison, United States. pp.CDROM, ⟨10.1109/SSP.2007.4301222⟩
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Séparation bayésienne de sources spectrales sous contraintes de positivité et d'additivitéXXIième Colloque GRETSI sur le Traitement du Signal et des Images, Sep 2007, Troyes, France. pp.CDROM
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Joint segmentation of piecewise constant autoregressive processes by using a hierarchical model and a Bayesian sampling approachIEEE International Conference on Acoustics Speech and Signal Processing (ICASSP 2006), May 2006, Toulouse, France. ⟨10.1109/ICASSP.2006.1660575⟩
Conference papers
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Hyperspectral EELS Image Unmixing6th Conference in Spectral Imaging (IASIM 2016), Jul 2016, Chamonix, France. 2016, Proceedings of the 6th Conference in Spectral Imaging (IASIM 2016)
Conference poster
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Perception et apprentissage de représentationsIntelligence artificielle - Regards croisés de chercheur·es, 44, La Dépêche; CNRS, pp.12-15, 2020, Le Petit Illustré
Book sections
hal-03114591v1
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Linear and nonlinear unmixing in hyperspectral imagingData Handling in Science and Technology, Resolving Spectral Mixtures with Applications from Ultrafast Time-Resolved Spectroscopy to Super-Resolution Imaging, 30 - 2016 (Chapter 6), , pp.185-224, 2016, Data Handling in Science and Technology, 978-0-444-63638-6. ⟨10.1016/B978-0-444-63638-6.00006-1⟩
Book sections
hal-02564834v1
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MCMC algorithms for supervised and unsupervised linear unmixing of hyperspectral imagesMary, David; Theys, Céline; Aime, Claude. New Concepts in Imaging: Optical and Statistical Models, 59, EDP Sciences, pp.381-401, 2013, EAS Publications Series, 978-2-7598-0958-5. ⟨10.1051/eas/1359017⟩
Book sections
hal-03609211v1
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Modèles bayésiens hiérarchiques pour le traitement multi-capteurTraitement du signal et de l'image [eess.SP]. Institut National Polytechnique de Toulouse - INPT, 2007. Français. ⟨NNT : ⟩
Theses
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