Universität Bielefeld - Sonderforschungsbereich 360
Bayesian Reasoning on Qualitative Descriptions
from Images and Speech
Gudrun Socher, Gerhard Sagerer, Pietro Perona
Abstract
Image Understanding denotes not only the ability to extract specific, non-numerical
information from images, but it implies also reasoning about the extracted information.
We propose a qualitative representation for image understanding results which is
suitable for reasoning Bayesian networks. Our representation is not purely qualitative
but enhanced with probabilistic information to represent uncertainties and errors
in the understanding of noisy sensory data. The probabilistic information is then
supplied to a Bayesian networks in order to find the most plausible interpretation.
We apply this approach for the integration of image and speech understanding to
find objects in a visually observed scene which are verbally described by a human.
Results demonstrate the performance of our approach.
Postscript-File (~795 k)
Anke Weinberger, 1997-11-20