Video Segmentation (IS Colloquium)
Compared to static image segmentation, video segmentation is still in its infancy. Various research groups have different tasks in mind when they talk of video segmentation. For some it is motion segmentation, some think of an over-segmentation with thousands of regions per video, and others understand video segmentation as contour tracking. I will go through what I think are reasonable video segmentation subtasks and will touch the issue of benchmarking. I will also discuss the difference between image and video segmentation. Due to the availability of motion and the redundancy of successive frames, video segmentation should actually be easier than image segmentation. However, recent evidence indicates the opposite: at least at the level of superpixel segmentation, image segmentation methodology is more advanced than what can be found in the video segmentation literature.
Biography: Thomas Brox received his Ph.D. in computer science from the Saarland University, Saarbrücken, Germany in 2005. During his studies he spent three months as a visiting researcher at the INRIA Sophia-Antipolis, France. After his Ph.D. he joined the Computer Vision Group at the University of Bonn. From October 2007 to October 2008 he headed the Intelligent Systems Group at the University of Dresden as a temporary faculty member. After two years as a postdoctoral fellow in the Computer Vision Group of Jitendra Malik at U.C. Berkeley he moved to the University of Freiburg, where he is heading the Computer Vision Group. Prof. Brox is associate editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence and the Image and Vision Computing journal. He was/is an area chair of ICCV 2011, ACCV 2014 and ECCV 2014, and reviews for several funding organizations. In 2004, he received the Longuet-Higgins Best Paper Award at the European Conference on Computer Vision. In 2011 he was awarded an ERC starting grant. He is interested in all aspects of computer vision with a focus on video analysis (optical flow estimation, video segmentation, and learning from videos).
