Aim
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Scientific objectives
An increasing number of application areas require the processing of massive datasets. These data can often be represented by graphs in order to encode complex interactions. When data vectors are associated with graph vertices or edges, a so-called graph signal is obtained. Processing such graph signals includes several open challenges because of the nature of the involved information. Indeed the combination of graph theory and signal / image processing methodologies has not been studied as much as it should. On the one hand, new development are needed in order to allow classical signal processing methods to work on irregular grids and non Euclidean spaces. On the other hand, considering the significant success of classical signal processing tools, it appears highly desirable to generalize their use to graph signals.
- Tutorial objectives
The summer school will be dedicated to young researchers (inculding PhD students) working at the interface of the three topics covered within the shool: signal and image processing, graph modelling and neuroimagery and willing to deal with this interdiscplinary challenges in depth. In comparison to classic lectures given within universities, this school will feature cross topic lectures highlighting the necessity to learn different topics at the same time. This summer school will gather together experts in theory and modelling to facilitate the contacts between students and experts of such various fields. By attending this summer school, participants will develop necessary interdisplinary skills in order to propose efficient methodology to be applied to graph signal processing.
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