Daniel Bardsley

This is more computer graphics than vision but the videos on Ron Fedkiw’s home page are simply amazing.

Fedkiw received his Ph.D. in Mathematics from UCLA in 1996 and did postdoctoral studies both at UCLA in Mathematics and at Caltech in Aeronautics before joining the Stanford Computer Science Department. He was awarded the National Academy of Science Award for Initiatives in Research, a Packard Foundation Fellowship, a Presidential Early Career Award for Scientists and Engineers (PECASE), a Sloan Research Fellowship, the ACM Siggraph Significant New Researcher Award, an Office of Naval Research Young Investigator Program Award (ONR YIP), a Robert N. Noyce Family Faculty Scholarship, two distinguished teaching awards, etc. Currently he is on the editorial board of the Journal of Computational Physics, Journal of Scientific Computing, SIAM Journal on Imaging Sciences, and Communications in Mathematical Sciences, and he participates in the reviewing process of a number of journals and funding agencies. He has published over 80 research papers in computational physics, computer graphics and vision, as well as a book on level set methods.
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From the Reactable homepage: “The reactable is a collaborative electronic music instrument with a tabletop tangible multi-touch interface. Several simultaneous performers share complete control over the instrument by moving and rotating physical objects on a luminous round table surface. By moving and relating these objects, representing components of a classic modular synthesizer, users can create complex and dynamic sonic topologies, with generators, filters and modulators, in a kind of tangible modular synthesizer or graspable flow-controlled programming language.”
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The recently released results of the Face Recognition Grand Challenge now show that computers are capable at outperforming humans in face recognition tasks. The recent match up of face-recognition algorithms showed that machine recognition of human individuals has improved tenfold since 2002 and a hundredfold since 1995. Indeed, the best face-recognition algorithms now perform more accurately than most humans can manage. Overall, facial-recognition technology is advancing rapidly.

Check out Mark Williams’s article for the Technology Review for a rundown of some of the entries for the grand challenge. This story was also recently covered on slashdot.