EMMCVPR'2003 - Lisbon, Portugal, July 7-9, 2003

Complete program

Minimization problems and optimization methods permeate computer vision (CV), pattern recognition (PR), and many other fields of  machine intelligence. This is because many approaches to CV&PR involve optimization tasks, and, at a more basic level, several problems can be cast as the minimization of some basic quantity (often called an energy).

Instances of (energy) minimization problems arise in Bayesian decision making, Markov random fields, relaxation labeling, neural networks, variational formulations, support vector machines, regularization, to mention only a few (not necessarily mutually exclusive) areas/frameworks of CV&PR, with roots in disciplines such as statistics, (statistical) physics, and psychophysics.

The aim of this workshop, which is the third of a series, is to bring together people with research interests in this interdisciplinary topic. Although the subject is traditionally well represented in major international conferences on CV&PR, this workshop provides a forum where researchers can report their recent work and engage in more informal discussions. As with the previous editions (1997 and 1999), the proceedings will be published by Springer Verlag in the Lecture Notes on Computer Science (LNCS) series. The submission instructions can be found here.

The scientific program of EMMCVPR-2001 will include the presentation of invited talks and contributed research papers. The workshop, which is sponsored by the  International Association for Pattern Recognition (IAPR),  will be organized by the Sophia-Antipolis research unit of INRIA (Institut National de Recherche en Informatique et en Automatique), in France. Sophia-Antipolis is located on the French Riviera (Côte d'Azur), near Nice, Antibes, and Cannes.

Important:Due to space constraints of the workshop site, attendance will be limited to 100.
 

A list of relevant topics includes (but is not restricted to):
 

Markov random fields Probabilistic networks / graphical models Variational formulations Deformable models Graph matching Statistical pattern recognition Supervised learning Unsupervised learning VC-theory and support vector machines  Information theoretic methods

Model selection Computational neurobiology Visual perception and psychophysics Neural networks for classification and regression Markov-Chain Monte Carlo methods Relaxation labeling Variational and mean-field methods Self-organizing networks Evolutionary / genetic approaches Applications

 

Invited speakers

Donald Geman, Whitaker Institute, Johns Hopkins University, USA,
                           and Centre de Math. et Leurs Applications, Ecole Normale Superieure, Cachan, France.
Geoffrey Hinton, Gatsby Computational Neuroscience Unit,  University College London, UK.
David Mumford, Division of Applied Mathematics, Brown University, USA.

• Co-chairs

Josiane Zerubia, INRIA Sophia Antipolis, France   (Josiane.Zerubia@sophia.inria.fr)
Anil K. Jain, Michigan State University, USA    (jain@cse.msu.edu)
Mario Figueiredo, Instituto Superior Tecnico, Portugal    (mtf@lx.it.pt)

• Program committee

Important Dates
 

Paper submission deadline (extended): February 10 , 2001

Notification of acceptance: April 30, 2001

Camera-ready paper due:   May 31, 2001

A plain text version of  this call for papers can be found here

Last update: May 3, 2001