Convex Optimization Methods for Computer Vision and Image Analysis

Ziel

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.

Finanzierungsplan

ERC-SG - ERC Starting Grant

Gastgebende Einrichtung

TECHNISCHE UNIVERSITAET MUENCHEN

EU-Beitrag

€ 1 985 400,00

Adresse

Arcisstrasse 21
80333 Muenchen
Deutschland

Region

Bayern Oberbayern München, Kreisfreie Stadt

Aktivitätstyp

Higher or Secondary Education Establishments

Hauptforscher

Daniel Cremers (Prof.)

Kontakt Verwaltung

Ulrike Ronchetti (Ms.)

Links

Die Organisation kontaktieren Website

Gesamtkosten

Keine Daten

Begünstigte (1)