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Large motion capture systems
This project addresses the problem of reconstructing the 3D motion of multiple objects  moving within a predefined volume from the videos of multiple cameras. In particular, the motion is captured by tracking a number of unlabeled markers placed on each target object. Each camera placed around the arena focuses only on a small portion of the total volume to achieve high resolution, and each point of the arena can be seen from multiple cameras to avoid cluttering. The motion of the objects can be reconstructed only by combining the videos from all cameras. Although motion marker-based motion capture is a well studied problem, the main novel challenge reside in the need of performing the 3D motion capture from hundreds of cameras in real-time. This poses severe problems due to obvious bandwidth constraints and to computational constraints. Therefore, the goal of this project to devise smart distributed tracking algorithms and adaptive cameras bundling that can take advantage of  smart cameras capable of autonomous computation and communication to reduce both computational burden and communication requirements.

Adaptive Optics

Adaptive optics (AO) is used in astronomy to obtain high resolution images, close to diffraction limited, of stars and galaxies with ground telescopes, otherwise blurred by atmospheric turbulence.

In order to succesfully compensate for the distorsion induced by athmospheric turbulence one (or more) deformable mirros are deformed; the control action used measurements of the wavefront distorsion.
Next generation AO systems have thousand of sensors and actuators and as such pose non-trivial issues both for modeling and control.

The research focusses on atmospheric turbulence modeling, simulation, and prediction.

Multivariate Image Analysis (MIA)
The aim of MIA is the automation of some industrial and chemical processes. The goal of our research is generilizing and reducing the computational complexity of previously proposed techniques.






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