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VASC Seminar

February

15
Mon
Ko Nishino Assistant Professor, Computer Science Drexel University
Monday, February 15
3:00 pm to 4:00 pm
Exploiting the Latent Structures of 3D Geometry and Appearance

Event Location: NSH 1507
Bio: Ko Nishino is an assistant professor in the Department of Computer Science at Drexel University. He received a B.E. and an M.E. in Information and Communication Engineering (ECE) in 1997 and 1999, respectively, and a PhD in Computer Science in 2002, all from The University of Tokyo. Before joining Drexel University in 2005, he was a Postdoctoral Research Scientist in the Computer Science Department at Columbia University. His primary research interests lie in computer vision and include appearance modeling and synthesis, geometry processing, and video analysis. His work on exploiting eye reflections received considerable media attention including articles in New York Times, Newsweek, and NewScientist. He received the NSF CAREER award in 2008.

Abstract: Images contain much more information than seen at first glance. Whether it is apparent to the naked eyes or not, they encode the intrinsic structures, i.e., the inherent variabilities, of the physical world. These latent structures, if extracted properly, provide rich information that leads to novel approaches to long-standing problems and enables new applications of visual processing. In this talk, I will demonstrate this in two different domains: 3D geometry and appearance. I will first discuss the modeling and use of geometric scale-variability, the size variation of local geometric structures comprising objects and scenes. I will show that, by exploiting this hidden dimension of 3D geometric data, novel applications such as reconstructing objects from a mixed pile of range images can be made possible. Next, I will discuss exploiting the variability of reflectance underlying real-world appearance by introducing a novel reflectance model that enables compact yet faithful characterizations of real-world materials and the space they span. I will show that this enables a sound probabilistic approach to radiometric scene decomposition, e.g., joint estimation of illumination and reflectance from an image, that remains challenging for arbitrary real-world objects and scenes.