CALL FOR PARTICIPATION
 
   ADVANCED SCHOOL ON COMPUTER VISION, PATTERN RECOGNITION AND
   IMAGE  PROCESSING
   (Organized by Vision, Image Processing and Sound Laboratory -
    Department of Computer Science - University of Verona) 	
 	
   May 17-20,2004
   Verona, Italy
 
 This school would like to be the first of a series of seminars, aiming
 at offering advanced lectures on significant topics related to Computer
 Vision, Pattern Recognition, and Image Processing. 
 
 These schools are particularly addressed to PhD students, but all types
 of researchers are welcome. They will typically hold one week on one
 specific topic, so that audience can install a more productive interaction
 with the lecturer.
 
 The maximum number of participants is limited to 50 persons. In case of
 a larger number of applicants, priority will be given to PhD students.
 
 This first school focuses on the statistical approach in computer
 vision, analyzing the graphical models for learning and inference with
 related applications. 
 
 Invited speaker:
 
 Prof. Brendan J. Frey, University of Toronto, Canada
 
 Course Title:
   Bayesian Networks and Algorithms for Inference and Learning:
   Applications in Computer Vision, Audio Processing, and Molecular
   Biology
 
 Description:
   Algorithms for automatically analyzing images, video, audio,
   communication signals, biological sequences, text, and other types of
   data should take into account the uncertain relationships between
   inputs, intermediate representations, and outputs. Probability theory
   can account for these uncertainties, and provides a way to pose
   information processing problems as the computational task of finding an
   appropriate probability model and computing conditional probabilities
   using the model. Complex probability models for real-world applications
   often involve millions of random variables and intractable density
   functions, so probabilities cannot be computed using straightforward
   approaches.
 
   This course examines the fundamental concepts of graph-based
   formulations of complex probability models and introduces
   computationally efficient techniques for computing probabilities and
   estimating parameters in these models. Although the course is a
   "fundamentals" course, we will study several impressive real-world
   applications.
 
 DAY 1
   Bayesian networks, Markov random fields and factor graphs. Computing
   probabilities in graphical models, the elimination algorithm and the
   sum-product algorithm. Generative models. Case study: Computer Vision.
 
 DAY 2
   Learning observed graphical models, the exponential family.
   Parameterized models, parameters as variables, models for
   classification, regression and clustering. Learning partially
   unobserved graphical models, free energy, iterative conditional modes,
   the EM algorithm. Case study: Molecular Biology.
 
 DAY 3
   Mixtures of Gaussians, HMMs, the multivariate Gaussian, factor
   analysis, linear dynamic systems, Kalman filtering and smoothing,
   learning linear dynamic systems. Variational techniques, the
   sum-product algorithm in graphs with cycles, Bethe free energy. Case
   study: Audio Processing. 
 
 DAY 4
   Monte Carlo methods, rejection sampling, adaptive rejection sampling,
   importance sampling, particle filters, Markov chain Monte Carlo
   methods, Gibbs sampling, Metropolis algorithm. Wrap-up. 
 
 Check out the following website for full details:
 http://vips.sci.univr.it/html/VIPSschool2004.html
 
 Please also distribute this to your PhD students/researchers/colleagues
 who might be interested.
 
 If there are any queries please email the following address:
 mailto:school_info@zeus.sci.univr.it
 
   >>>> Registration deadline is 30 April 2004 <<<<<
 
   Directors: Prof. Vittorio Murino, Dr. Andrea Fusiello
   Local Organizers: Andrea Colombari, Marco Cristani, Michela Farenzena