Fourth International Workshop on Energy Minimization Methods in 
Computer Vision and Pattern Recognition - EMMCVPR 2003

July 7-9, 2003                     
Lisbon, Portugal


Call for Papers

Many problems in computer vision and pattern 
recognition (CVPR) are couched in the framework of 
optimization. The minimization of a global quantity, often 
referred to as the energy, forms the bulwark of most 
approaches in CVPR. Disparate approaches such as 
discrete and probabilistic formulations on the one hand, 
and continuous, deterministic strategies on the other, often 
have optimization or energy minimization as a common 
theme. Instances of energy minimization arise in 
Gibbs/Markov modeling, Bayesian theory, geometric and 
variational approaches and in areas in CVPR such as 
object recognition/retrieval, image segmentation, 
registration, reconstruction, classification and data mining.

The aim of this workshop, the fourth in a series, is to bring 
together researchers with interests in these disparate areas, 
but with an underlying commitment to some form of 
optimization. Although the subject is traditionally well 
represented in major conferences on CVPR, this workshop 
provides a forum wherein researchers can report their 
recent work and engage in more informal discussions. As 
with the previous editions (1997, 1999, and 2001) the 
proceedings will be published by Springer Verlag in the 
Lecture Notes on Computer Science (LNCS) series. 

The scientific program of EMMCVPR-2003 will include 
invited talks and contributed research papers. The 
workshop is sponsored by the International Association 
for Pattern Recognition and will be held in Instituto 
Superior Tecnico (IST), Technical University of Lisbon, 
co-organized by the Institute of Telecommunications (IT).

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

        Gibbs/Markov modeling 
        Probabilistic networks and graphical models 
        Variational formulations, level sets, and PDEs 
        Deformable models and registration 
        Graph matching 
        Statistical pattern recognition 
        Supervised and unsupervised learning 
        VC-theory and support vector machines 
        Information theoretic methods and model selection
        Combinatorial optimization 
        Interior point methods 
        Image reconstruction and coding 
        Markov-Chain Monte Carlo methods 
        Relaxation labeling 
        Advanced mean-field methods 
        Self-organizing networks 
        Evolutionary / genetic approaches 
        Applications

Co-chairs

Mario Figueiredo,  IT and IST, Portugal 
Anand Rangarajan,  University of Florida, USA    
Josiane Zerubia,  INRIA, France    


Program committee 

P. Aguiar, ISR and IST, Portugal
Y. Amit, University of Chicago, USA 
Y. Boykov, Siemens  Research, USA
J. Buhmann, Univ. of Bonn, Germany  
R. Chin, U. of Science and Technology, Hong Kong 
L. Cohen, U. Paris-Dauphine, France
J. Dias, IT and IST, Portugal
B. Dom, IBM Almaden Research Center, USA 
M.-P. Dubuisson-Jolly, Siemens Research, USA 
D. Geiger, New York University, USA 
G. Gimel'farb, Univ. of Auckland, New Zealand
C. Graffigne, Univ. Rene Descartes,France  
E. Hancock, University of York, UK  
T. Ho, Bell Laboratories, USA 
V. Murino, Univ. of Verona, Italy
R. Nowak Rice University, USA
M. Pelillo, University of Venice, Italy 
J. Principe, Univ. of Florida, USA
K. Siddiqi, McGill University, Canada 
R. Szeliski, Microsoft Research, USA  
A. Trouve, Univ. Paris 13, France
B. Vemuri, Univ. of Florida, USA
L. Younes, ENS Cachan, France
A. Yuille, UCLA, USA  
R. Zabih, Cornell University, USA 
S.-C. Zhu, UCLA, USA 
 
Invited speakers

Bill Freeman, MIT, USA. 
Alfred Hero, University of Michigan, USA. 
Panos Pardalos, University of Florida, USA

Important dates

Paper submission deadline:  January 6, 2003
Notification of acceptance:   March 1, 2003
Camera-ready paper due:    April 1, 2003

For submission instructions and other information 
please visit our website:
http://www.emmcvpr.org