GlobalVision

Global Optimization Methods in Computer Vision, Pattern Recognition and Medical Imaging

Computer vision concerns itself with understanding the real world through the analysis of images. Typical problems are object recognition, medical image segmentation, geometric reconstruction problems and navigation of autonomous vehicles. Such problems often lead to complicated optimization problems with a mixture of discrete and continuous variables, or even infinite dimensional variables in terms of curves and surfaces. Today, state-of-the-art in solving these problems generally relies on heuristic methods that generate only local optima of various qualities. During the last few years, work by the principal investigator, co-workers, and others has opened new possibilities. This research project builds on this. We will in this project focus on developing new global optimization methods for computing high-quality solutions for a broad class of problems. A guiding principle will be to relax the original, complicated problem to an approximate, simpler one to which globally optimal solutions can more easily be computed. Technically, this relaxed problem often is convex. A crucial point in this approach is to estimate the quality of the exact solution of the approximate problem compared to the (unknown) global optimum of the original problem.

ERC Starting Grant.
Principal Investigator: Fredrik Kahl.
Funded by the European Research Council.
Period: 2008-2013.

Project publications

Below is a selection of the publications within the project.

A Brute-Force Algorithm for Reconstructing a Scene from Two Projections
Enqvist, Olof; Jiang, Fangyuan; Kahl, Fredrik, 2011, Published at: Conference on Computer Vision and Pattern Recognition, Fulltext:

Generalized Roof Duality for Pseudo-Boolean Optimization
Kahl, Fredrik; Strandmark, Petter, 2011, Published at: International Conference on Computer Vision, Fulltext:

Optimization for Multi-Region Segmentation of Cardiac MRI
Ulén, Johannes; Strandmark, Petter; Kahl, Fredrik, 2011, Published at: MICCAI Workshop on Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, Fulltext:

Parallel and Distributed Vision Algorithms Using Dual Decomposition
Strandmark, Petter; Kahl, Fredrik; Schoenemann, Thomas, 2011, Published in: Computer Vision and Image Understanding, Fulltext:

Optimizing Parametric Total Variation Models
Strandmark, Petter; Kahl, Fredrik; Overgaard, Niels Chr., 2009, Published at: International Conference on Computer Vision, Fulltext: