Civita Vellucci

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Photometric redshifts (photo-z’s) are fundamental in galaxy surveys to address different topics, from gravitational lensing and dark matter distribution to galaxy evolution. The Kilo Degree Survey (KiDS), i.e. the ESO public survey on the VLT Survey Telescope (VST), provides the unprecedented opportunity to exploit a large galaxy dataset with an exceptional(More)
In HRI applications, tracking performance should not be evaluated as a passive sensing behavior, but by considering it as an active process, where the human is involved within the loop. We foresee that the presence of the human being, actively participating in the interaction, improves a tracker performance with a limited additional effort. We tested a(More)
Photometric redshifts (photo-z's) provide an alternative way to estimate the distances of large samples of galaxies and are therefore crucial to a large variety of cosmological problems. Among the various methods proposed over the years, supervised machine learning (ML) methods capable to interpolate the knowledge gained by means of spectroscopical data(More)
People detection and tracking are essential capabilities in human-robot interaction (HRI). Typically, a tracker performance is evaluated by measuring objective data, such as the tracking error. However, in HRI applications, human- tracking performance does not have to be evaluated by considering it as a passive sensing behavior, but as an active sensing(More)
Context. The Kilo-Degree Survey (KiDS) is an ongoing optical wide-field imaging survey with the OmegaCAM camera at the VLT Survey Telescope. It aims to image 1500 square degrees in four filters (ugri). The core science driver is mapping the large-scale matter distribution in the Universe, using weak lensing shear and photometric redshift measurements.(More)
A variety of fundamental astrophysical science topics require the determination of very accurate photometric redshifts (photo-z). A wide plethora of methods have been developed, based either on template models fitting or on empirical explorations of the photometric parameter space. Machine-learning-based techniques are not explicitly dependent on the(More)
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