CONVEXVISION

Convex Optimization Methods for Computer Vision and Image Analysis

Coordinatore	TECHNISCHE UNIVERSITAET MUENCHEN    more Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie.
Nazionalità Coordinatore	Germany [DE]
Totale costo	1˙985˙400 €
EC contributo	1˙985˙400 €
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-2009-StG
Funding Scheme	ERC-SG
Anno di inizio	2010
Periodo (anno-mese-giorno)	2010-09-01   -   2015-08-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	TECHNISCHE UNIVERSITAET MUENCHEN  Organization address address: Arcisstrasse 21 city: MUENCHEN postcode: 80333 contact info Titolo: Ms. Nome: Ulrike Cognome: Ronchetti Email: send email Telefono: +49 89 289 22616 Fax: +49 89 289 22620	DE (MUENCHEN)	hostInstitution	1˙985˙400.00
2	TECHNISCHE UNIVERSITAET MUENCHEN  Organization address address: Arcisstrasse 21 city: MUENCHEN postcode: 80333 contact info Titolo: Prof. Nome: Daniel Cognome: Cremers Email: send email Telefono: -734559 Fax: -734561	DE (MUENCHEN)	hostInstitution	1˙985˙400.00

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 Obiettivo del progetto (Objective)

'Optimization methods have become an established paradigm to address most Computer Vision challenges including the reconstruction of three-dimensional objects from multiple images, or the tracking of a deformable shape over time. Yet, it has been largely overlooked that optimization approaches are practically useless if they do not come with efficient algorithms to compute minimizers of respective energies. Most existing formulations give rise to non-convex energies. As a consequence, solutions highly depend on the choice of minimization scheme and implementational (initialization, time step sizes, etc.), with little or no guarantees regarding the quality of computed solutions and their robustness to perturbations of the input data. In the proposed research project, we plan to develop optimization methods for Computer Vision which allow to efficiently compute globally optimal solutions. Preliminary results indicate that this will drastically leverage the power of optimization methods and their applicability in a substantially broader context. Specifically we will focus on three lines of research: 1) We will develop convex formulations for a variety of challenges. While convex formulations are currently being developed for low-level problems such as image segmentation, our main effort will focus on carrying convex optimization to higher level problems of image understanding and scene interpretation. 2) We will investigate alternative strategies of global optimization by means of discrete graph theoretic methods. We will characterize advantages and drawbacks of continuous and discrete methods and thereby develop novel algorithms combining the advantages of both approaches. 3) We will go beyond convex formulations, developing relaxation schemes that compute near-optimal solutions for problems that cannot be expressed by convex functionals.'