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Probabilistic 3D Object Recognition.

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I. Shimshoni and J. Ponce.

Proc. International Conference on Computer Vision, 1995.

### Abstract

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We present a probabilistic 3D object recognition algorithm
which uses the probabilistic peaking effect of measured angles and
ratios of lengths to guide the recognition process. Isoratio and
isoangle curves are traced on the viewing sphere and are used to
compute conditional probabilities. These probabilities are
incorporated into a probabilistic model which deals with various types
of uncertainty associated with the recognition process, such as
uncertainty due to imperfect results of edge and corner detection
(e.g. detecting edges which belong to the background, not detecting
edges which belong to the object, and uncertainty in edge and corner
position). We perform pose estimation and compute the uncertainty in
pose due to the uncertainty in measurements in the image. The
uncertainty pose subspaces are used to find hypotheses which reinforce
each other. We compute the probability that these sets of hypotheses
are correct and use these probabilities to rank the matching
hypotheses.
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