CONVEXVISION
Convex Optimization Methods for Computer Vision and Image Analysis
| Coordinatore | TECHNISCHE UNIVERSITAET MUENCHEN
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
| # | ||||
|---|---|---|---|---|
| 1 |
TECHNISCHE UNIVERSITAET MUENCHEN
Organization address
address: Arcisstrasse 21 contact info |
DE (MUENCHEN) | hostInstitution | 1˙985˙400.00 |
| 2 |
TECHNISCHE UNIVERSITAET MUENCHEN
Organization address
address: Arcisstrasse 21 contact info |
DE (MUENCHEN) | hostInstitution | 1˙985˙400.00 |
Mappa
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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.'