Chiara Bassetti

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Detection of groups of interacting people is a very interesting and useful task in many modern technologies, with application fields spanning from video-surveillance to social robotics. In this paper we first furnish a rigorous definition of group considering the background of the social sciences: this allows us to specify many kinds of group, so far(More)
BACKGROUND AND OBJECTIVES Literature review suggests that the sentinel lymph node (sN) represents a reliable predictor of axillary lymph node status in breast cancer patients; however, some important issues, such as the optimisation of the technique for the intraoperative identification of the sN, the role of intraoperative frozen section examination of the(More)
We propose a new type of crowd analysis, focused on the spectator crowd, that is, people “interested in watching something specific that they came to see” [1]. This scenario applies on stadiums, amphitheaters etc., and shares some aspects with classical crowd monitoring: actually, many people are simultaneously observed, so that perperson analysis is hard;(More)
PURPOSE To evaluate the physical imaging characteristics of an indirect digital radiography system used for general radiography. MATERIAL AND METHODS The performance of the two 41x41 cm2 CsI:Tl/a-Si flat-panel detectors of a GE-Revolution XR/d digital radiography system was evaluated. Signal uniformity, dose linearity, pre-sampling and expectation(More)
The topic of crowd modeling in computer vision usually assumes a single generic typology of crowd, which is very simplistic. In this paper we adopt a taxonomy that is widely accepted in sociology, focusing on a particular category, the spectator crowd, which is formed by people “interested in watching something specific that they came to see”(More)
We focus on the automated analysis of spectator crowd, that is, people watching sport contests alive (in stadiums, amphitheaters etc.), or, more generally, people “watching the activities of an event [. . . ] interested in watching something specific that they came to see” [2]. This scenario differs substantially from the typical crowd analysis setting(More)
Understanding whether an event attracted the audience’s attention and which moments were mostly enjoyable is a primary goal for sport and show business managers. OZ (Osservare l’attenZione – Observing attention) is an interdisciplinary, mixed-methods project that aims at developing a technology able to automatically detect at run time spectators’ attention(More)
There is an error in the first sentence of the Evaluation Metrics subsection of the Experiments section. The correct sentence is: As accuracy measures, we adopt the metrics proposed in [3] and extended in [12]: we consider a group as correctly estimated if at least d(T jGj)e of their members are found by the grouping method and correctly detected by the(More)
Resulting from an interdisciplinary endeavor, the paper proposes an ontological model for supporting collaborative work practices in critical settings, and shows its application to a specific domain. The model is empirically-grounded, as based on ethnographic research carried out at an international airport –clearly an example of safety-critical(More)