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Learning Probabilistic Appearance Models for Object Recognition
Presented by: Stephanie Fonder
As an alternative to reconstructing the 3D information as
a model for object recognition, it is possible to use a set
of 2D views as a model. Here the model has two levels, one
corresponding to clusters of images representing different
views and a second representing smaller variations due to
presence, position, and measurements of features of the objects
appearance. This is accomplished using incremental conceptual
clustering algorithm to form the clusters and a generalization
algorinthem for making a single description from the cluster
of images. Information from features and their relations are
used to speed up the search for a match in the model.
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