Course Description
( upcoming/recent | all | 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006)Learning and Probabilistic Techniques in Computer Vision (Seminar)
ULg, Liège, Belgium, April 12?13, 2007.
This seminar is coordinated by Justus H. Piater, in the framework of the CIL Doctoral School and the MUSICS Graduate School.
For detailed information and registration, please consult the web page of the seminar : www.montefiore.ulg.ac.be/~piater/courses/sem-learn/
Topics
Over the past decade, probabilistic techniques have become of primordial importance in computer vision, and the boundaries between machine learning and computer vision become increasingly blurred. Familiarity with these concepts is thus essential for effective research in computer vision. The objective of this seminar is to provide an introduction to basic techniques and to some of the most important current methods.
The exact selection of topics and applications covered, as well as the balance of basic vs. advanced concepts depend on the participants. Examples of possible topics include Bayesian networks, Hidden Markov Models, belief propagation, particle filtering, probabilistic latent semantic analysis, classification, recognition, matching, structural models.