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Peter Belhumeur: Lessons from Photographing and Identifying the World's Plant Species

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Published on Apr 13, 2011

From ICCP11 Hosted by Carnegie Mellon University, Robotics Institute
April 8, 2011

Abstract:

Columbia University, the University of Maryland, and the Smithsonian Institution are working on visual recognition software to help identify species from photographs. I will discuss our work on?developing Leafsnap -- the first in a series of electronic field guides. As part of this work, we have completed photographing close to one third of the world's plant species and have begun capturing beautiful high-resolution images of live specimens. Our?work has led us in many new research directions in different domains such as human faces and, most recently, architecture, including the adoption of centuries-old techniques from taxonomy for the process of labeling images with visual attributes. In particular, I will show that it is possible to automatically recognize a wide range of visual attributes in images and use them in numerous applications of computational photography.

Bio:

Peter N. Belhumeur is currently a Professor in the Department of Computer Science at Columbia University and the Director of the Laboratory for the Study of Visual Appearance (VAP LAB). He received a Sc.B. in Information Sciences from Brown University in 1985. He received his Ph.D. in Engineering Sciences from Harvard University under the direction of David Mumford in 1993. He was a postdoctoral fellow at the University of Cambridge's Isaac Newton Institute for Mathematical Sciences in 1994. He was made Assistant, Associate and Professor of Electrical Engineering at Yale University in 1994, 1998, and 2001, respectively. He joined Columbia University as a Professor of Computer Science in 2002. His research focus lies somewhere in the mix of computer vision, computer graphics, and computational photography. He is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) and the National Science Foundation Career Award. He won both the Siemens Best Paper Award at the IEEE Conference on Computer Vision and Pattern Recognition and the Olympus Prize at the European Conference of Computer Vision.

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