VISREC

Visual Recognition

 Coordinatore THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD 

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 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


 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

image    visual    scenes    content    materials    recognition    actions    objects    video    annotating    searching   

 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.'

Altri progetti dello stesso programma (FP7-IDEAS-ERC)

MACRONETS (2014)

Production Networks in Macroeconomics

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HORIZOMS (2009)

New Horizons for Mass Spectrometry

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MORIAE (2009)

HUMAN AND MOUSE MODELS OF RHINOVIRUS INDUCED ACUTE ASTHMA EXACERBATIONS

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