Use of Belief Networks for Modeling Indoor Environments

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ConferenceProceedings of Vision Interface '97, May 19-23, 1997., Kelowna, British Columbia, Canada
AbstractThis article introduces an approach, based on Bayesian Networks, for the grouping of 3-D surfaces extracted from data obtained by a laser ranging sensor. A methodology for the specification of the network is presented along with an approach for determining the conditional probabilities. Determination of the conditional probabilities is based on a compatibility function that measures the uncertainty in the quality of fit of the data to a model of the features in the scene. Several compatibility functions for the grouping of 3-D surfaces are presented. These are co planarity, parallel, planarity, and proximity. These compatibility functions are used with a Bayesian Network in determining belief values of possible groupings among the surfaces, in particular grouping for continuous surfaces and corners. This operation is a form of perceptual grouping of three dimensional data and is akin to the previous studies in perceptual grouping for two dimensional images.
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AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number40174
NPARC number8913249
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Record identifier40107f03-8ba6-4f86-933c-cb9fd0ddaeb9
Record created2009-04-22
Record modified2016-05-09
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