| Description |
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
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