Computer vision

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Face detection

The aim of computer vision and image analysis is to make computers understand images, videos and other image-like data. Given that the human vision system is one of the main sensory organs, enabling computers to see and understand images has a wide range of applications; example applications include interpreting a traffic scene in order to safely drive a vehicle, recognizing an object and its pose to be grasped by a robot arm, and localizing a tumor in a 3D magnetic resonance image. 

Machine learning has enabled some recent major breakthroughs in computer vision, which have pushed the research frontier and led the way to human-like performance for many applications. Still, there are many research challenges ahead that need to be addressed.

Complex problem issues

The problems arising in computer vision are complex and require a wide spectrum of tools from geometry, statistics and numerical methods. To a large extent, the research in our group is concerned with developing new methods and theory. It can be the analysis of an optimization problem that is central in machine learning or the development of suitable mathematical models. There are also projects related to industrial applications. We work closely with other research groups nationally and internationally, within our field and with experts in medicine, robotics and mathematics. We also work with industry, ranging from start-ups to research collaborations with large companies.

Research areas