Computer Science

Computer Science Colloquium

Im Rahmen des Informatik-Kolloquiums, das von den Instituten des Fachbereichs Informatik, der Österreichischen Gesellschaft für Informatik (ÖGI), der Arbeitsgemeinschaft für Datenverarbeitung (ADV) sowie der Österreichischen Computergesellschaft (OCG) abgehalten wird, spricht

Prof. Dr. Vincent Lepetit

Graz University of Technology

über das Thema:

Image Features for Robust Registration

Zeit: 2015-05-05 16:00:00.0, 60 Minuten
Ort: Science Park I room MT130


Extracting image features such as feature points or edges is a critical step of many Computer Vision systems, however this is still performed with carefully handcrafted methods. In this talk, I will present a new Machine Learning-based approach to detecting local image features, with application to contour detection in natural images, but also biomedical and aerial images, and to feature point extraction under drastic weather and lighting changes. I will then show that it is also possible to learn efficient object description based on low-level features for scalable 3D object detection.


Dr. Vincent Lepetit is a Professor at the Institute for Computer Graphics and Vision, TU Graz and a Visiting Professor at the Computer Vision Laboratory, EPFL. He received the PhD degree in Computer Vision in 2001 from the University of Nancy, France, after working in the ISA INRIA team. He then joined the Virtual Reality Lab at EPFL as a post-doctoral fellow and became a founding member of the Computer Vision Laboratory. He became a Professor at TU GRAZ in February 2014. His research interests include vision-based Augmented Reality, 3D camera tracking, Machine Learning, object recognition, and 3D reconstruction. He often serves as program committee member and area chair of major vision conferences (CVPR, ICCV, ECCV, ACCV, BMVC). He is an editor for the International Journal of Computer Vision (IJCV) and the Computer Vision and Image Understanding (CVIU) journal.
Einladender: Prof. Dr Oliver Bimber, Institute of Computer Graphics
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