Research Projects (2010)

Smartphones have evolved considerably in processing power over the last years. They now feature multi-core CPUs as well as GPUs and consumer-quality cameras up to HD resolution. This makes them an interesting platform for graphics and vision and opens new opportunities for research.

The aim of AR4DOC is to facilitate the task of document inspection by a human operator. This requires the person to have detailed knowledge about the nature of a document, which may be outdated or even unavailable at the time of inspection.

We seek to provide this information in an interactive way using Mobile Augmented Reality (AR), so that a well-grounded decision on the vailidity of a document is possible. This involves several tasks such as document localization, recognition, tracking, presentation as well as interaction.

This research project is devoted to the study of higher order convex variational methods for problems in computer vision. First order methods, i.e. methods which take into account first order derivatives have shown a great success for a variety of inverse computer vision problems. This success is mostly due to the introduction of total variation methods by Rudin, Osher and Fatemi in 1992. Total variation methods exhibit the important property to preserve sharp discontinuities in the solution while the associated optimization problem is still convex. This leads to robust problem solutions, independent of any initialization. Besides this, total variation methods also exhibit some disadvantages. First, total variation methods favor piecewise constant solutions which leads to staircaising artifacts in image restoration problems and to the preference of fronto‐parallel structures in stereo problems. Second, total variation methods introduce a shrinking bias in shape optimization problems. The aim of this project is therefore to study higher order convex variational methods in order to improve the shortcomings of first order methods. We therefore propose to investigate two approaches. The first approach is based on the so‐called generalized total variation method, recently introduced by Bredies, Kunisch and Pock. It provides a framework to recover piecewise polynomial functions based on a convex functional. We expect that this method leads to significant improvements of stereo and motion estimation problems. The second approach is based on the so‐called roto‐translation space introduced by Citti and Sarti in 2006. It allows to rewrite functionals incorporating curvature regularity by means of a convex first order functional in higher dimensions. We expect that this approach will significantly improve the performance of various shape optimization problems.

The focus of the project is outdoor mobile computer vision with all of its challenges. Mobile systems need to be compact and energy efficient and are frequently changing locations. Therefore they must be autonomous and perform processing locally. A number of challenges arise from these requirements for which the project aims to provide solutions: Being compact, there is not much space for a large number of sensors such as laser scanners, radar antennas and the like. The work in this project will focus on stereo vision but with two different types of cameras. Often a second camera is already available and stereo information increases detection accuracies. Each time the system moves it needs to adapt to the changing situation. This requires adaptive calibration and online learning. Mobile systems often work from batteries. In addition, there is not much space to include intricate cooling systems. Thus, the system must be designed to be very energy efficient. New approaches for dynamic power management will be explored in the project. To put the work into context, several applications from the area of traffic surveillance/toll enforcement will be implemented and tested in an application oriented setting.

Current traffic enforcement solutions are either very large and costly (section control, toll enforcement) or do not offer much in terms of image processing (radar speed control). The technological output of Mobi Trick makes it possible to design mobile solutions for traffic monitoring, vehicle identification and classification, intelligent incident detection and observation of driver behavior. Mobile devices are also more efficient in enforcement. Their transient nature makes them less predictable. Mobile systems can also react more flexibly to changing road situations such as construction sites.

The market for mobile media services will expand significantly in the next years. The explosion in the usage of smartphones and the growth of the application store model to sell individual services to smartphone users opens a new and attractive market for developers of simple, useful applications. New revenue streams can be created by in-application one-click purchasing. Aggregation of a camera on Internet-connected smartphones leads to the possibility of having a live video stream of the user's reality augmented by content and services from the Web. Location-based services and augmented reality are seen as potential killer applications of the mobile Internet because users are enabled to access additional information related to where they are, what they are seeing, or what they are doing, as well as instantly purchase related services and content.

For example, instead of just seeing a street poster for a club night and passing by, this new paradigm opens up instant access via the Internet-enabled smartphone to the club‘s location, purchasing an entrance ticket or listening to/buying the DJ mixes. A new co-operation net-work – the Innovation Network for Smart Applications and Media - will bring key Austrian R&D and innovative SMEs together to make real this new paradigm for smart mobile and media applications which we call Smart Reality, and be the first to benefit commercially from it.