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###
Recognizing Volumetric Models
in the Presence of Uncertainty

*T. Arbel, F.P. Ferrie*
Classically, the problem of object recognition has long been formulated
as that of recognizing the instance of some object in a scene through information
acquired from an image. This has motivated such questions as how to compute
stable and effective representations for objects from image data and how
to correctly identify an unknown object from its representation. Our interest,
and the primary focus of this research, is how such decisions can be made
in the presence of uncertainty and how one can explicitly represent and
reason about the uncertainty. We have proposed a framework for parametric
shape recognition based on a probabilistic model of inverse theory first
introduced by Tarantola in [Tarantola:87]. The key result is a method for
generating classifiers in the form of conditional probability densities
in order to recognize an unknown from a set of reference models. Our procedure
is automatic. Off-line, it invokes an autonomous process to estimate reference
model parameters and their statistics. On-line, during measurement, it
combines these with a priori context-dependent information, as well as
the parameters and statistics estimated for an unknown object, into a single
description. That description, a conditional probability density function,
represents the likelihood of correspondence between the unknown and a particular
reference model. The original context of this work was three-dimensional
object recognition in which objects were represented by parametric shape
descriptors and their uncertainties. Since then we have extended the approach
to include parametric representations computed from other imaging modalities.
Generally the process is the same. Object models are computed from a bottom-up
analysis leading to a set of descriptors which form the basis for recognition.
An experimental system has been implemented in which objects are represented
by either single or multiple parametric models. These experiments have
indicated that recognition performance is near perfect for cases in which
most of the object is visible to the observer, and falls off gracefully
(minimal false-positive response) when only partial information is available.

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*Annual Report*

*Fri Nov 26 23:00:32 GMT 1999*