Rama Chellappa
Minta Martin Professor of Engineering University of Maryland, College Park MD 20742
"Compressive Sensing for Computer Vision: Hype vs Hope"
In this talk, I will present some main results from Compressive Sensing (CS) that are relevant to computer vision and discuss how CS ideas can be used for designing new algorithms for background subtraction, reconstruction from gradient fields, secure iris recognition and image formation.
 Rama Chellappa received the B.E. (Hons.) degree from the University of Madras, India, in 1975 and the M.E. (Distinction) degree from Indian Institute of Science, Bangalore, in 1977. He received the M.S.E.E. and Ph.D. Degrees in electrical engineering from Purdue University, West Lafayette, IN, in 1978 and 1981 respectively. Since 1991, he has been a Professor of electrical engineering and an affiliate Professor of computer science at University of Maryland, College Park. He is also affiliated with the Center for Automation Research (Director) and the Institute for Advanced Computer Studies (Permanent member). Currently, he holds a Minta Martin Professorship in the College of Engineering. Prior to joining the University of Maryland, he was an Assistant (1981-1986), Associate Professor (1986-1991) and Director of the Signal and Image Processing Institute (1988-1990) at University of Southern California, Los Angeles. Over the last 27 years, h has published numerous book chapters, peer-reviewed journal and conference papers in image and video processing, analysis and recognition. He has also co-edited/co-authored six books on neural networks, Markov random fields, face/gait-based human identification and activity modeling. His current research interests are face and gait analysis, 3D modeling from video, automatic target recognition from stationary and moving platforms, surveillance and monitoring, hyper spectral processing, image understanding, and commercial applications of image processing and understanding.
Alexei EfrosSchool of Computer Science
Carnegie Mellon University
"What can the world tell us about the image?"
Reasoning about a scene from a photograph is an inherently ambiguous task. This is because a single image in itself does not carry enough information to disambiguate the world that it is depicting. Of course, humans have no problems understanding photographs because of all the prior visual experience they can bring to bear on the task. How can we help computers do the same? Our solution is to use large amounts of visual data, both labeled and unlabeled, as a way of capturing the statistics of the natural world. In this talk, I will present some of our recent results on inferring geometric, photometric, and geographic scene properties from a single image. I will first briefly describe our system for estimating the rough geometric surface layout of a scene. I will show how this information, in turn, can be useful for modeling objects in the scene. Next, I will describe an approach for using the surface layout information as a way of estimating a rough illumination map for the scene. Finally, I will describe a new system that uses millions of unlabeled photographs from Flickr to capture some implicit geographic scene structure of an image.  Alexei (Alyosha) Efros is an assistant professor at the Robotics Institute and the Computer Science Department at Carnegie Mellon University. His research is in the area of computer vision and computer graphics, especially at the intersection of the two. He is particularly interested in using data-driven techniques to tackle problems which are very hard to model parametrically but where large quantities of data are readily available. Alyosha received his Ph.D. in 2003 from the University of California, Berkeley and spent the following year as a fine fellow at Oxford, England. Alyosha is a recipient of the NSF CAREER award (2006), the Sloan Fellowship (2008), and the Guggenheim Fellowship (2008).
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Keynote Speakers
