Event Location: NSH 1507
Bio: Jana Kosecka is an Associate Professor at the Department of Computer
Science, George Mason University. She obtained her M.S.E. in Electrical
Engineering and Computer Science from Slovak Technical University and
Ph.D. in Computer Science from University of Pennsylvania in 1996. In
1996 – 1999 she was a postdoctoral fellow at the EECS Department at
University of California, Berkeley. She is the recipient of David Marr’s
prize (with Y. Ma, S. Soatto and S. Sastry) and received the National
Science Foundation CAREER Award. Jana is a former Associate Editor of
IEEE Transactions on Robotics. Currently she is a Member of the
Editorial Board of International Journal of Computer Vision and
Associate Editor of IEEE Transactions on Pattern Analysis and Machine
Intelligence. She held visiting professor positions at UC Berkeley,
Stanford University and Google. She has over 70 publications in refereed
journals and conferences and is a co-author of a monograph titled
Invitation to 3D vision: From Images to Geometric Models. Her general
research interests are in Computer Vision, Machine Learning and
Robotics. In particular she is interested ‘seeing’ systems engaged in
autonomous tasks, acquisition of static and dynamic models of
environments by means of visual sensing and human-computer interaction.

Abstract: Recent advances in techniques for capturing large scale models of urban
environments, give rise to novel applications which require rapid and
realistic 3D modelling. I will present an 3D reconstruction approach
utilizing properties of piecewise planarity to suppress the ambiguities
causing failures of standard dense stereo methods in problematic
scenarios containing many repetitive structures and no or low textured
regions.

Using the same type of representation, I will also introduce some
on-going work on semantic parsing of urban scenes using spatial
co-occurence of visual words and 3D geometry. I will show some
preliminary results on challenging environments with varying viewpoints
and large number of categories appearing simultaneously.