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8th VIPS Advanced School on
Computer Vision, Pattern Recognition
and Image Processing

Old Verona, Piazza Isolo
(click >>here<< for a free tourist guide and infos about Verona)

Click here for a tourist tour!
June 18-21, 2007
Organized by the Vision, Image Processing and Sound Laboratory
Department of Computer Science, University of Verona, Italy

NEWS 13/06/2007: (Only) today we know the exact schedule for the lessons (see below).

This is the 8th Advanced School organized by the VIPS laboratory, the eighth of a series of advanced lectures on significant topics in Computer Vision, Pattern Recognition, and Image Processing.

These courses are particularly addressed to PhD students, but open to all types of researchers. Each course will typically be held in at most one week and will be focused on one specific topic in order to provide a more productive interaction with the lecturer.

The maximum number of participants is limited to 50 persons. In case of a larger number of applications, priority will be given to PhD students.

This school is titled "Modeling natural signals with statistical hierarchical models". Details about the course, contents and the registration procedure are given in the following.

The 8th Advanced School is supported by GIRPR, (Gruppo Italiano Ricercatori in Pattern Recognition)


Vittorio Murino
Andrea Fusiello

Local Organizers

Marco Cristani
Michela Farenzena
Dong Seon Cheng
Riccardo Gherardi
Davide Moschini
Alessandro Perina


Dr. Nebojsa Jojic

Microsoft Corporation
One Microsoft Way, Redmond, WA 98052

Course title

Modeling natural signals with statistical hierarchical models


The course will cover probabilistic inference techniques and modeling strategies that allow machine learning approaches for automatic extraction of medium level representations of natural signals. By structuring statistical generative models to mimic the structure of the real world, the models should be able to automatically adapt to audio, visual or multimodal signals during the unsupervised model fitting (learning) stage, thus providing a medium-level representation suitable for compression, transmission, search, editing, enhanced viewing experience, etc. These models are object-based, where an object can produce sounds, have a changing appearance, move and be exposed to attenuation in audio domain, illumination in video domain, and, when other objects are present, to occlusion or additive mixing in both domains. Adaptivity is the main requirement to these models. For example, the same model should be applicable to tracking a person in front of a cluttered background, and to tracking a flock of birds. The tracking task, as well as many other tasks performed jointly, such as de-noising, dynamic mosaic building or object removal as well as separating audio sources and associating them to object appearances, are all achievable as probabilistic queries, i.e., inference of the hidden variables associated to the world structure. All this should be doable using the data itself, without special application-specific initialization procedure or the separate supervised training stage.

The course will also cover modeling biological data, such as biological sequences, binding energy data, and crystal structure data, as well as one example of probabilistic inference applied to a highly refined example of sequence data: human-generated machine code.

  • 1st Day - Monday, 18th of June 2007 - Graphical Models

    • Bayesian networks
    • Markov random fields and factor graphs
    • Simple inference techniques
    • Generative models
    • Case study: Computer Vision
  • 2nd Day - Tuesday, 19th of June 2007 - Advanced Inference and Learning

    • Parameterized models, parameters as variables, models for classification, regression and clustering
    • Learning partially unobserved graphical models, free energy, iterative conditional modes, sampling methods, variational methods and the EM algorithm
  • 3rd Day - Wednesday, 20th of June 2007 - Some General-Purpose Graphical Models

    • Mixtures of Gaussians
    • Hidden Markov Models
    • The multivariate Gaussian
    • Factor analysis
    • Linear dynamic systems, Kalman filtering and smoothing, learning linear dynamic systems
  • 4th Day - Thursday, 20th of June 2007 - The Art of Modeling

    • Occlusion models for visual and auditory data
    • Epitomes for vision, audio and biology applications
    • Modeling molecular binding
    • Deformable spectrograms for audio representation

Final Lectures Schedule - The lessons will be held in "Aula Verde", which is located in our department (see below the information on how to reach it)

Monday 18 Tuesday 19 Wednesday 20 Thursday 21
9.00 - 12.30 9.00 - 12.30 9.00 - 12.30
14.00 - 17.00 14.00 - 17.00 14.00 - 17.00 14.00 - 17.00

Course Fees

200 euro for PhD and undergraduate students.
250 euro for post doc, researchers, and other people working directly in a university.
300 euro for everybody else.


If you are interested, you must send an email to in which you ask for participation. Please, state your identity and your status (undergraduate, PhD student, other) and wait for the confirmation email. The ultimate deadline is June 6, 2007.

Attached to our confirmation email you will find a registration form to print, compile and send together with a proof of the payment by fax before June 6, 2007, to the following no. +39 045 8027068, to the attention of Prof. V. Murino, 8th VIPS School on Computer Vision, Pattern Recognition, and Image Processing.

The proposed payment method is bank wire transfer (all necessary data are in the form).

Important Dates

Registration deadline:

June 1, 2007


Course Fee payment deadline:

June 6, 2007

(Registration form + Proof of payment)


June 18-21, 2007


The accomodation costs are not covered by the Course Fee. However, we have made agreements with some convenient hotels and you can find a list of available places here.
If you wish to take advantage of these opportunities please remember to notify to the hotel that you are attending our school.

Information on how to reach our department are presented in this page.

For any other information, please send an email to

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Last revision: 13 June, 2007