Learning vision is easy?
10 May 2022, 17:00:00
The visual cortex is a major area in the human brain and the majority of input from the environment is perceived by vision. Still, it was believed for decades that computer vision is a simple problem that can be addressed by student projects or clever system design based on concepts from related fields. About a decade ago, the field observed a major breakthrough with the advent of deep learning, to a large extent enabled by means of novel computational resources in form of GPUs.
Since then, the field basically exploded and thousands of black-box approaches to solve vision problems have been developed. However, it turned out that previously known concepts such as uncertainty and geometry are still relevant when we want to better understand deep learning methods and achieve state of the art performance.
Michael has gotten his PhD in Computer Vision from Christian-Albrechts Universtät in Germany and his Professor in Computer Vision at Linköping University. In 2018 he was awarded the highest ranked AI researcher in Sweden.
Now he is working on a wide range of topics within artificial visual systems, such as three-dimensional computer vision, computational imaging, object detection, tracking and recognition, robot vision and autonomous systems.