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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.
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/.