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Event Entry
New for: D2, D3
What and Who
The Information is in the Maps
Prof. Leonidas Guibas
Stanford University
Talk
Leonidas Guibas obtained his Ph.D. from Stanford under the supervision of Donald Knuth. His main subsequent employers were Xerox PARC, Stanford, MIT, and DEC/SRC. He has been at Stanford since 1984 and is currently the Paul Pigott Professor of Computer Science (and by courtesy, Electrical Engineering). He heads the Geometric Computation group and is part of the Graphics Laboratory, the AI Laboratory, the Bio-X Program, and the Institute for Computational and Mathematical Engineering. Professor Guibas' interests span computational geometry, geometric modeling, computer graphics, computer vision, robotics, ad hoc communication and sensor networks, and discrete algorithms --- all areas in which he has published and lectured extensively. Some well-known past accomplishments include the analysis of double hashing, red-black trees, the quad-edge data structure, Voronoi-Delaunay algorithms, the Earth Mover's distance, Kinetic Data Structures (KDS), Metropolis light transport, and the Heat-Kernel Signature. Professor Guibas is an ACM Fellow, a winner of the ACM Allen Newell award, and was named to the Paul Pigott Professorship.
AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, MMCI
Public Audience
English
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Date, Time and Location
Friday, 4 February 2011
14:00
60 Minutes
E1 4
024
Saarbrücken
Abstract
Geometric data in the form of 3D scans, images, videos, or GPS traces is becoming abundantly available on the Web and increasingly important to our economy and life. The usual pipeline in transforming such data to useful models involves data analysis operations such as feature extraction, interpolation, smoothing, fitting, segmentation, etc. In this talk we argue for a different perspective on understanding geometric data that is a based on the study of informative mappings between different data sets, within a single data set, or from a data set to a simpler space that captures its essential structure. The computation of such good mappings leads to interesting but challenging optimization problems. When our data acquisition samples the world in a dense fashion, correlations between multiple data sets create networks of maps that provide additional information both about the structure of the data as well as about the acquisition process itself. We present examples of this approach for understanding isometries between 3D scans, or for connecting large image corpora into useful webs through map networks.
Contact
Christina Fries
-103
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Christina Fries, 01/10/2011 15:15 -- Created document.