- Vivi Padova
- Il bo
Machine Learning is about endowing artificial systems the ability to learn from experience. Our research in this field spans a broad range of topics and includes building models from measured data as well as learning actions from "experience". When focusing on learning dynamical models this is often referred to as System identification.
Computer vision is the science of retrieving information from images and movies (sequences of images indexed by time). Our goal is to develop methodologies to enable artificial systems to use visual sensors (such as cameras) to interact with the environment similarly to what us humans do. Basic tasks we have in mind are navigation in an unknown and unstructured environment, recognition and classification of objects and scenes, surveillance etc.
Spectral estimation is the science of building models in the frequency domain from measured data. Our research focuses on the development of spectral estimation techniques building models with high resolution in prescribed frequency bands. Finally, these techniques have been successfully applied to image compression.