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The probabilistic vision group is part of the McGill Centre for Intelligent machines. The focus of our research is on developing probabilistic inference techniques for computer vision problems. Such problems often involve processing noisy data; probabilistic approaches are then appropriate as they allow for uncertainty to be modeled and propagated through the solution process. Choosing to represent the solution to a computer vision problem as a probability distribution over many possible solutions makes it possible to measure the ambiguity of the results and provide guidelines as to whether more data should be acquired, when possible. Applications include, but are not limited to

  • Object recognition
  • Active vision
  • Image segmentation
  • Image registration
  • Image-guided neurosurgery

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    Faculty

        Professor Tal Arbel

    Students

        Cathy Laporte (Ph. D.)
        Nagesh Subbanna (Ph. D.)
        Dante De Nigris (Ph. D.)
        Colm Elliott (Ph. D.)
        Meltem Demirkus (Ph. D.)
        Zahra Karimaghaloo (Ph. D.)
        Thierry Bélanger-Roy (M. Eng.)

    Post-doctoral fellows

        Mohak Shah

    Alumni

        Rola Harmouche (now Ph. D. student at École Polytechnique de Montréal)
        Frank Riggi (now with Micro Target Media Interactive, Buffalo NY)
        Matt Toews (now postdoctoral fellow at Harvard)
        Rupert Brooks (now with NRC, Boucherville)


    L to R: Cathy, Colm, Meltem, Tal, Thierry, Mohak, Dante

     

    Medical Imaging Laboratory
    McGill Center For Intelligent Machines
    3480 University Street Room 412
    Montréal, Québec
    Canada H3A 2A7
    Telephone (514) 398-8702
    Fax (514) 398-7348
    Email @cim.mcgill.ca