Web and Multimedia

Web and Multimedia Team

Rich Internet Applications for collective intelligence

The research is mainly conducted in Lab 2. The goal is to design and develop Rich and Intelligent Internet Applications, desktop and mobile, exploiting the possibilities of machine learning on big data, collective intelligence, user profiling and sensor information.

The team is composed by: Roberto Caldelli, Andrea Ferracani, Andrea Del Mastio, Daniele Pezzatini, Paolo Mazzanti, Giuseppe Becchi.

UMETECH

Media Technology for Cultural Heritage

2017-2019
funded by: European commition

This project aims to strengthen the role of Latin American Universities as instruments of social and economic development in the cultural heritage sector through the design and implementation of 4 competence centers on cultural heritage specialised in: smart computing, 3d, big data and human-machine interaction.

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SEE FOR ME

A smart mobile audio-guide

2016-2017
supported by: Regione Toscana

Our smart audio guide perceives the context and is able to interact with users: it performs automatic recognition of artworks, to enable a semi-automatic interaction with the wearer. The system is backed by a computer vision system capable to work in real-time on a mobile device, coupled with audio and motion sensors. The system has been deployed on a NVIDIA Jetson TK1 and a NVIDIA Shield Tablet K1, and tested in a real world environment (Bargello Museum of Florence).
Video available here.

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NEMECH – New Media for Cultural Heritage

Competence Center on Cultural Heritage

2013-today
supported by: Regione Toscana

NEMECH is a Competence Centre on cultural heritage established by the Region of Tuscany and activated by MICC – University of Florence .

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Image and Video Annotation by exploiting Social Media

Tagging and Retrieval of Social Media

2015-2016

The success of media sharing and social networks has led to the availability of extremely large quantities of images that are tagged by users. The need of methods to manage efficiently and effectively the combination of media and metadata poses significant challenges. In this project, we perform social media analysis and propose methods for automatic annotation of social images and videos.

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