Robust Optimization Techniques in Computer Vision

This half-day tutorial will be given in conjunction with the European Conference on Computer Vision 2014 in Zurich, Switzerland, in the morning of 7 September 2014. It is a newly developed course including both classical methods such as RANSAC and more recent approaches based on branch & bound and convex optimization for robust model estimation and the problem of handling outliers in computer vision.

The tutorial is organized by

Course description

Many important problems in computer vision, such as structure from motion and image registration, involve model estimation in presence of a significant number of outliers. Due to the outliers, simple estimation techniques such as least squares perform very poorly. To deal with this issue, vision researchers have come up with a number of techniques that are robust to outliers, such as Hough transform and RANSAC (random sample consensus). These methods will be analyzed with respect to statistical modeling, worst-case and average exectution times and how to choose the balance between the number of outliers and the number of inliers. Apart from these classical techniques we will also describe recent advances in robust model estimation. This includes sampling based techniques with guaranteed optimality for low-dimensional problems and optimization of semi-robust norms for high-dimensional problems. We will see how to solve low-dimensional estimation problems with over 99% outliers in a few seconds, as well as how to detect outliers in structure from motion problems with thousands of variables.


Slides: Session 1, Session 2 in ppt with animations, Session 2 in pdf without animations, Session 3.


Half-day course. Time: Morning, 7 September, 2014.

There will be three sessions, following the topic description above.


We target both beginners in the field of computer vision and more experienced researchers interested in learning more about recent advances.

Distributed materials

All course notes and slides will be distributed to the attendees.

Speaker bios

Olof Enqvist got his MSc degree from Linköping University in 2006, and a PhD in mathematics from Lund University in 2011. He currently works as assistant professor at Chalmers University in Göteborg, Sweden. A large part of his research has been dealing with outliers and optimization in computer vision problems and he has published several papers on the topic at ECCV, ICCV and CVPR.

Fredrik Kahl received his MSc degree in computer science in 1995 and his PhD in mathematics in 2001, both from Lund University, Sweden. He was a postdoctoral research fellow at the Australian National University in 2003-2004 and at the University of California, San Diego in 2004-2005. He currently has a joint professor position at Chalmers University of Technology and Lund University. Primary research areas include geometric computer vision problems, medical image analysis and optimization methods. In 2005, he was awarded the Marr Prize for work on multiple view geometry, in 2008 he obtained an ERC Starting Independent Research Grant from the European Research Council, and the same year, he received a Future Research Leader Grant from the Swedish Foundation for Strategic Research.

Richard Hartley is currently with the Computer Vision Group at the Research School of in- formation Sciences and Engineering (RSISE), Australian National University (ANU), Canberra, and also with National ICT Australia (NICTA), a government-funded research institute. He worked in the areas computer-aided electronic design system, described in his book Digit Serial Computation, and computer vision, particularly in multiview geometry. He is the winner of the Significant Senior Computer Vision Researcher Award at ICCV 2011. He was the program chair for ICCV 2013 (Sydney) and a fellow of the Australian Science Academy.