S5 (2013-2014)

Much of modern data processing requires identification of low-dimensional structures in high-dimensional spaces, using observations that are incomplete or noisy. This general paradigm applies to the restoration of images (where natural images form a low-dimensional subset of the space of … Read More

Much of modern data processing requires identification of low-dimensional structures in high-dimensional spaces, using observations that are incomplete or noisy. This general paradigm applies to the restoration of images (where natural images form a low-dimensional subset of the space of … Read More

Convex optimisation and stochastic sampling are two powerful methodologies for performing statistical inference in inverse problems related to signal and image processing. It is widely acknowledged that these methodologies can complement each other very well; yet they are generally studied … Read More

Jet engines rank amongst the most complex machines ever built and are governed by deterministic and stochastic phenomena. Since jet engines are subject to extremely demanding operating conditions, a proper maintenance is critical to ensure high safety, maximum availability and … Read More

We address the representation of two-dimensional shapes in its most general form, i.e., arbitrary sets of points. Examples of these shapes arise in multiple situations, in the form of sparse sets of representative landmarks, or dense sets of image edge … Read More