Mario Beaulieu, B.Sc.

Senior Research Agent – Vision and Imaging

Mario Beaulieu
Team: Vision and Imaging

T 514 840-1235, ext. 2478
@ mario.beaulieu@crim.ca

(B.Sc. Physics, Université de Montréal, 1995)

Mario’s main expertise is satellite image processing. His career includes academic and industrial experiences and projects, from Université de Montréal’s remote sensing lab to contracts with the Canadian Space Agency that led him to work for LBGI and GPA.

Mario joined the CRIM in 2000. Since then, he has contributed to various projects that include the development of detection algorithms for anatomical structures in ophthalmic imaging, the elaboration of fusion techniques applied to very high resolution satellite images, the creation of aerial photo mosaics and the use of image characterization in order to facilitate the identification of shooting locations.

More recently, Mario has been actively implicated in projects on the optimization of subtitle positioning techniques for the deaf and hearing-impaired and on the automatic updating of hydric zones in satellite images through active contour models.

Mario is currently finishing his master’s degree at Université de Montréal. His master’s thesis is on the use of deep learning techniques in remote sensing.

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Recent news

  • Best wishes for the holidays!
    18/12/2019

    Happy Holidays from all of us at CRIM!

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Upcoming event

  • Transformation numérique - Secteur public - Événement Les Affaires
    29 January 2020 7:00
    DoubleTree par Hilton, Montréal
    Le CRIM fier partenaire de la 1ère édition sur la Transformation numérique - Secteur public organisée par les Événement Les Affaires, qui se tiendra les 29 janvier 2020 au DoubleTree par Hilton, Montréal.
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  • Nous y serons!! https://t.co/CxzO3DDoUy
  • Infolettre de janvier 2020 ????pour avoir les dernières nouvelles du CRIM, ses membres et partenaires.… https://t.co/8sKn8SXVe0

Recent Publications

  • On the use of Fisher Vector Encoding for Voice Spoofing Detection

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  • Deep learning speech recognition for low-resource languages

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