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.
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.
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)
200 euro for PhD and undergraduate
If you are interested, you must send an email to firstname.lastname@example.org 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).
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
Information on how to reach our department are presented in this page.
For any other information, please send an email to email@example.com