Analyzing Impossible Images

Steve Seitz
University of Washington

Slides (ppt, pdf)

Many of the greatest advances in imaging technology (motion pictures, electron microscopes, MRI, etc.) enabled us to capture and see the world in ways that were not previously possible. It's reasonable to expect that imaging breakthroughs of the future will continue to bend the limits of what we can and cannot see and produce new types of (previously) impossible images.

Impossible images have exciting applications in the field of computer vision. For example, analysis could be vastly simplified if there were no occlusions, i.e., if it were possible to see all sides of an object or scene in a single image. Similarly, cameras that remove complex illumination effects such as shadows and interreflections could dramatically enhance the performance of existing algorithms. This talk will explore the creation of such images and their implications for computer vision and graphics.

Bio sketch:

Steve Seitz is an Associate Professor in the Department of Computer Science and Engineering at the University of Washington. He received his B.A. in computer science and mathematics at the University of California, Berkeley in 1991 and his Ph.D. in computer sciences at the University of Wisconsin, Madison in 1997. Following his doctoral work, he spent one year visiting the Vision Technology Group at Microsoft Research, and subsequently two years as an Assistant Professor in the Robotics Institute at Carnegie Mellon University. He joined the faculty at the University of Washington in July 2000. He was twice awarded the David Marr Prize for the best paper at the International Conference of Computer Vision, and has received an NSF Career Award, an ONR Young Investigator Award, and an Alfred P. Sloan Fellowship. Professor Seitz is interested in problems in computer vision and computer graphics. His current research focuses on capturing the structure, appearance, and behavior of the real world from digital imagery