VISREC

Visual Recognition

Coordinatore	THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD    more Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie.
Nazionalità Coordinatore	United Kingdom [UK]
Totale costo	1˙872˙056 €
EC contributo	1˙872˙056 €
Programma	FP7-IDEAS-ERC Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	ERC-2008-AdG
Funding Scheme	ERC-AG
Anno di inizio	2009
Periodo (anno-mese-giorno)	2009-01-01   -   2014-12-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD  Organization address address: University Offices, Wellington Square city: OXFORD postcode: OX1 2JD contact info Titolo: Ms. Nome: Gill Cognome: Wells Email: send email Telefono: +44 1865 270201 Fax: +44 1865 289800	UK (OXFORD)	hostInstitution	1˙872˙056.00
2	THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD  Organization address address: University Offices, Wellington Square city: OXFORD postcode: OX1 2JD contact info Titolo: Prof. Nome: Andrew Cognome: Zisserman Email: send email Telefono: -284877 Fax: -274831	UK (OXFORD)	hostInstitution	1˙872˙056.00

Mappa

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 Obiettivo del progetto (Objective)

'Our goal is to develop the fundamental knowledge to design a visual system that is able to learn, recognize and retrieve quickly and accurately thousands of visual categories, including materials, objects, scenes, human actions and activities. A ``visual google' for images and videos -- able to search for the ``nouns' (objects, scenes), ``verbs' (actions/activities) and adjectives (materials, patterns) of visual content. The time is right for making great progress in automated visual recognition: imaging geometry is well understood, image features are now highly developed, and relevant statistical models and machine learning algorithms are well-advanced. Our goal is to make a quantum leap in the capabilities of visual recognition in real-life scenarios. The outcomes of this research will impact any applications where visual recognition is useful, and will enable new applications entirely: effortlessly searching and annotating home image and video collections on their visual content; searching and annotating large commercial image and video archives (e.g. YouTube); surveillance; using an image, rather than text, to access the web and hence identify its visual content.'