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Convex Optimization Methods for Computer Vision and Image Analysis

Obiettivo

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.

Invito a presentare proposte

ERC-2009-StG
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Meccanismo di finanziamento

ERC-SG - ERC Starting Grant

Istituzione ospitante

TECHNISCHE UNIVERSITAET MUENCHEN
Contributo UE
€ 1 985 400,00
Indirizzo
Arcisstrasse 21
80333 Muenchen
Germania

Mostra sulla mappa

Regione
Bayern Oberbayern München, Kreisfreie Stadt
Tipo di attività
Higher or Secondary Education Establishments
Ricercatore principale
Daniel Cremers (Prof.)
Contatto amministrativo
Ulrike Ronchetti (Ms.)
Collegamenti
Costo totale
Nessun dato

Beneficiari (1)