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Abstract:
Estimating motion in scenes containing multiple moving
objects remains a difficult problem in computer vision. A promising
approach to this problem involves using mixture models, where the
motion of each object is a component in the mixture. However,
existing methods typically require specifying in advance the number
of components in the mixture, i.e. the number of objects in the
scene. Here we show that the number of objects can be estimated
automatically in a maximum likelihood framework, given an
assumption about the level of noise in the video sequence. We
derive analytical results showing the number of models which
maximize the likelihood for a given noise level in a given
sequence. We illustrate these results on a real video sequence,
showing how the phase transitions correspond to different
perceptual organizations of the scene.
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