Accès direct au contenu

MATH

Version anglaise

aide

Accueil > Formations > Master MVA > Présentation des cours

Object recognition and computer vision

Lecturer : Ivan Lpatev, Jean Ponce, Cordelia Schmid, Josef Sivic, (ENS Ulm)

Objective of the course :

Automated object recognition - and more generally scene analysis - from photographs and videos is the great challenge of computer vision. The objective of this course is to provide a coherent introductory overview of the image, object and scene models, as well as the methods and algorithms, used today to address this challenge.

Topics :

  •  Instance-level recognition: camera geometry, local-invariant features, correspondence, efficient visual search
  •  Category-level recognition: bags of features, sparse coding and dictionary learning
  •  Neural networks, optimization methods
  •  Convolutional neural networks for visual recognition
  •  Motion and human actions
  •  3D object recognition
  •  Weakly-supervised learning for visual recognition

Link to the new page :http://www.di.ens.fr/willow/teaching/recvis16/

Prerequisites :

Basic linear algebra, analysis and probability

Organization of courses :

  • 10 lectures of 3 hours each.
  • All materials and lectures will be in English. Reports from assignments and the final project can be done in French or English.

Plus d'infos ici...

Validation :

There will be three programming assignments representing 50% of the grade and a research-oriented final project (including report and presentation) representing 50% of the grade.

    References :

    • D.A. Forsyth and J. Ponce, "Computer Vision: A Modern Approach'', Prentice-Hall, 2nd edition, 2011
    • J. Ponce, M. Hebert, C. Schmid, and A. Zisserman, "Toward Category-Level Object Recognition'', Lecture Notes in Computer Science 4170, Springer-Verlag, 2007.
    • R. Szeliski, "Computer Vision: Algorithms and Applications", Springer, 2010, Available online: http://szeliski.org/Book/.