Broad Area Colloquium For AI-Geometry-Graphics-Robotics-Vision
Visual Recognition for Perceptive Interfaces
May 8, 2006, 4:15PM
Devices should be perceptive, and respond directly to their human user
and/or environment. In this talk I'll present new computer vision
algorithms for fast recognition, indexing, and tracking that make this
possible, enabling multimodal interfaces which respond to users'
conversational gesture and body language, robots which recognize
common object categories, and mobile devices which can search using
visual cues of specific objects of interest. I'll describe in detail
a method for image indexing and recognition of object categories based
on a new kernel function over sets of local features that approximates
the true correspondence-based similarity between set elements. Our
pyramid match efficiently forms an implicit partial matching between
two sets of feature vectors. The matching has linear time complexity
and is robust to clutter or outlier features--a critical advantage for
handling images with variable backgrounds, occlusions, and viewpoint
changes. With this technique, mobile devices can recognize locations
and gather information about newly encountered objects by finding
matching images on the web or other available databases.
About the Speaker
Trevor Darrell is an Associate Professor of Electrical Engineering and Computer Science at M.I.T. He leads the Vision Interface Group at the Computer Science and Artificial Intelligence Laboratory. His interests include computer vision, interactive graphics, and machine learning. Prior to joining the faculty of MIT he worked as a Member of the Research Staff at Interval Research in Palo Alto, CA, reseaching vision-based interface algorithms for consumer applications. He received his PhD and SM from the MIT Media Lab in 1996 and 1991, and the BSE while working at the GRASP Robotics Laboratory at the University of Pennsylvania in 1988.
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