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WORLDCOMP'09 Tutorial: Prof. Ali Mohammad-Djafari

Last modified 2009-07-04 00:25


Inverse Problems in Imaging Systems and Computer Vision: From Deterministic Regularization to Probabilistic Bayesian Approaches
Prof. Ali Mohammad-Djafari
Directeur de recherche au CNRS
Centre National de la Recherche Scientifique (CNRS), France

Date: July 13, 2009
Time: 5:30-9:00 PM
Location: Silver Room


Abstract

    Inverse problems arise in many imaging and computer vision systems: image denoising, restoration and reconstruction, super-resolution, fusion or separation. In many imaging applications such as medical imaging or non destructive testing (2D, 3D, 2D+time or 3D+time) describing the problem as an inverse problem is natural, because we have measured data which are related to the unknown quantities through a physical model. In computer vision the problems such as stereo, image fusion, 3D scene reconstruction from shadows or from photographies at different angles, satellite imaging, etc., can also easily be written as inverse problems. We can also write many problems such as Blind source separation, Compressed sensing, multi or hyper spectral image segmentation as inverse problems and parameter estimation. A common framework for all these problems can be written in an algebraic form and then easily compare the deterministic regularization theory and the probabilistic Bayesian inference frameworks.


Objectives

    Give a general mathematical framework for a great number of signal and image processing problems such as: Image denoising, restoration, super-resolution, segmentation, compression, separation and classification, encountered in many application area such as: Computed Tomography (medical imaging and industrial Non Destructive Testing) and Radar and SAR imaging. The outline of the tutorial is:

    • Examples of inverse problems in different area and applications
    • Description in a common mathematical framework 3- Deterministic regularization theory
    • Probabilistic methods
    • Bayesian inference and estimation framework
    • Prior models: from simple separable and Markovian to complex and hierarchical Markovian models with hidden Markovian fields
    • A Computer tomography example where the interest of the Bayesian approach with a Gauss-Markov-Potts prior modeling is shown.

Intended Audience

    All researchers interested by signal and image processing, particularly those who are familiar with inverse problems, regularization theory and probabilistic inference and statistical methods.


Biography of Instructor

    Ali Mohammad-Djafari was born in Iran. He received the B.Sc. degree in electrical engineering from Polytechnique of Teheran in 1975, the Engineering diploma degree (M.Sc.) from "Ecole Supérieure d'Electricité (SUPELEC)", Gif sur Yvette, France in 1977, the "Docteur-Ingénieur" (Ph.D.) degree and Doctorat d'Etat in Physics from "Université Paris Sud 11 (UPS)", Orsay, France, respectively in 1981 and 1987. He was Associate Professor at UPS for two years (1981-1983). Since 1984, he has a permanent position at "Centre National de la Recherche Scientifique (CNRS)" and works at "Laboratoire des Signaux et Systèmes (L2S)" at "SUPELEC". From 1998 to 2002, he has been at the head of Signal and Image Processing division at this laboratory. In 1997-1998. He has been visiting Associate Professor at University of Notre Dame, Indiana, USA. Presently, he is "Directeur de recherche" and his main scientific interests are in developing new probabilistic methods based on Bayesian inference, Information theory and Maximum entropy approaches for inverse problems in general, and more specifically for signal and image reconstruction and restoration. His recent research projects contain: Blind Sources Separation (BSS) for multivariate signals (satellite images, hyper spectral images), Data and Image fusion, Super resolution, X ray Computed Tomography, Microwave imaging, SAR imaging and Spatio-temporal Positron Emission Tomography (PET) data and image processing. The main application domain of his interests are Computed Tomography (X rays, PET, SPECT, MRI, Eddy current imaging, Ultrasound, Microwave, Radar and SAR Imaging) either for medical imaging or for Non Destructive Testing (NDT) in industry.

    Selected recent publications:

      A Mohammad-Djafari (2008) Gauss-Markov-Potts Priors for Images in Computer Tomography Resulting to Joint Optimal Reconstruction and segmentation International Journal of Tomography & Statistics 11: W09. 76-92

      A Mohammad-Djafari (2008) Super-Resolution : A short review, a new method based on hidden Markov modeling of HR image and future challenges The Computer Journal doi:10,1093/comjnl/bxn005:

      O Féron, B Duchêne, A Mohammad-Djafari (2005) Microwave imaging of inhomogeneous objects made of a finite number of dielectric and conductive materials from experimental data Inverse Problems 21: 6. 95-115 Dec

      Ch Soussen, A Mohammad-Djafari (2004) Polygonal and polyhedral contour reconstruction in computed tomography IEEE Trans. on Image Processing 13: 11. 1507-1523 Nov.

      A Mohammad-Djafari, J F Giovannelli, G Demoment, J Idier (2002) Regularization, Maximum Entropy and Probabilistic Methods in Mass Spectrometry Data Processing Problems Int. Journal of Mass Spectrometry 215: 1-3. 175-193 APR

      Prof. Ali Mohammad-Djafari
      Directeur de recherche au CNRS Laboratoire des signaux et systèmes (UMR 8506 CNRS-SUPELEC-UPS) SUPELEC,
      plateau de Moulon, 3 rue Joliot-Curie,
      91192 GIF-SUR-YVETTE Cedex (France)
      Tel: 01 69 85 17 41
      Fax : 01 69 85 17 65
      http://djafari.free.fr
      http://ali.djafari.lss.supelec.fr
      http://publicationslist.org/djafari

Academic Co-Sponsors

United States Military Academy, Network Science Center


Biomedical Cybernetics Laboratory, HST of Harvard University and MIT, USA


Argonne's Leadership Computing Facility of Argonne National Laboratory

Functional Genomics Laboratory, University of Illinois at Urbana-Champaign, USA
Minnesota Supercomputing Institute, University of Minnesota, USA
Intelligent Data Exploration and Analysis Laboratory, University of Texas at Austin, Austin, Texas, USA
Harvard Statistics Department Genomics & Bioinformatics Laboratory, Harvard University, USA

Texas Advanced Computing Center, The University of Texas at Austin, Texas

Center for the Bioinformatics and Computational Genomics, Georgia Institute of Technology, Atlanta, Georgia, USA

Bioinformatics & Computational Biology Program, George Mason University, Virginia, USA


Institute of Discrete Mathematics and Geometry, Vienna University of Technology, Austria

BioMedical Informatics & Bio-Imaging Laboratory, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
Knowledge Management & Intelligent System Center (KMIS) of University of Siegen, Germany

National Institute for Health Research, UK


Hawkeye Radiology Informatics, Department of Radiology, College of Medicine, University of Iowa, Iowa, USA

Institute for Informatics Problems of the Russian Academy of Sciences, Moscow, Russia.
Medical Image HPC & Informatics Lab (MiHi Lab), University of Iowa, Iowa, USA
SECLAB An inter-university research group (University of Naples Federico II, the University of Naples Parthenope, and the Second University of Naples, Italy)
The University of North Dakota, Grand Forks, North Dakota, USA
Intelligent Cyberspace Engineeing Lab., ICEL, Texas A&M; University (Com./Texas)

International Society of Intelligent Biological Medicine


World Academy of Biomedical Sciences and Technologies



Corporate Sponsor



Other Co-Sponsors
European Commission
High Performance Computing for Nanotechnology (HPCNano)

HoIP - Health without Boundaries


Hodges' Health

The International Council on Medical and Care Compunetics

GridToday - enewsletter focused on Grid, SOA, Virtualization, Storage, Networking and Service-Oriented IT


HPCwire - The Leading Source for Global News and Information Covering the Ecosystem of High Productivity Computing

The UK Department for Business, Enterprise & Regulatory Reform
VMW Solutions Ltd.
Scientific Technologies Corporation

Bentham Science Publishers


 


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