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In this talk I will present the portfolio of work we conduct in our lab. Herby, I will present three recent body of work in more detail. This is firstly our work on learning 6D Object Pose estimation and Camera localizing from RGB or RGBD images. I will show that by utilizing the concepts of uncertainty and learning to score hypothesis, we can improve the state of the art. Secondly, I will present a new approach for inferring multiple diverse labeling in a graphical model. Besides guarantees of an exact solution, our method is also faster than existing techniques. Finally, I will present a recent work in which we show that popular Auto-context Decision Forests can be mapped to Deep ConvNets for Semantic Segmentation. We use this to detect the spine of a zebrafish, in case when little training data is available.
Carsten Rother (TU Dresden)
Head of Computer Vision Lab Dresden
Carsten Rother received the diploma degree with distinction in 1999 from the University of Karlsruhe, Germany, and the PhD degree in 2003 from the Royal Institute of Technology Stockholm, Sweden. From 2003 until 2013 he was researcher with Microsoft Research Cambridge, UK. Since October 2013 he is full (W3) Professor at TU Dresden, heading the Computer Vision Lab Dresden (CVLD). His research interests are in the fields of computer vision and machine learning, and in particular optimization and learning in Probabilistic Graphical Models and Deep Learning. On the application side this involves scene understanding (e.g. autonomous driving, robotics and HCI), 6D object pose estimation, Bio-Imaging (e.g. cell tracking), Image editing (e.g. interactive image segmentation and deconvolution) and image matching (e.g. large displacement Scene Flow). He has published over 120 articles (H-index 58) at international conferences and journals. He won awards at BMVC ’16, ACCV ’14, CVPR ’13, BMVC ’12, ACCV ’10, CHI ’07, CVPR ’05, and Indian Conference on Computer Vision ’10. He was awarded the DAGM Olympus prize in 2009. He has co-developed two Microsoft products, GrabCut for Office 2010 and AutoCollage. He also co-authored a book on Markov Random Fields in Computer Vision and Image Processing. He serves as area chair for major conferences, and he is associated editor for T-PAMI.