Learning In Computer Vision

Bir Bhanu (UC Riverside) (I)

This talk will present an overview of learning in computer vision and will focus
on learning for content-based image retrieval application. It will present
(a) statistical learning techniques for relevance feedback, and (b) techniques
for exploitation of meta knowledge for learning visual concepts in image databases.


Bir Bhanu is Professor of EECS and the Director of the Center for Research in
Intelligent Systems (CRIS) at the University of California. at Riverside. He has
been the principal investigator of various programs for DARPA, NASA, NSF, AFOSR,
ARO and other agencies and industries in the areas of learning and vision, image
understanding, pattern recognition, target recognition, navigation, image databases,
and machine vision applications. He holds 10 U.S. and international patents and
over 200 reviewed technical publications in the areas of his interest. He received
the S.M. and E.E. degrees in electrical engineering and computer science from the
Massachusetts Institute of Technology, Cambridge, and the Ph.D. degree in
electrical engineering from the Image Processing Institute, University of Southern
California, Los Angeles. He is a Fellow of IEEE, AAAS, IAPR and Honeywell Inc.

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