Generic Model Abstraction from Examples
Sven Dickinson
Department of Computer Science
University of Toronto
Dienstag, 25.09.2001, 14 Uhr c.t., D6-135
The recognition community has long avoided bridging the representational
gap between traditional, low-level image features and generic models.
Instead, the gap has been artificially eliminated by either bringing the
image closer to the models, using simple scenes containing idealized,
textureless objects, or by bringing the models closer to the images,
using 3-D CAD model templates or 2-D appearance model templates. In this
paper, we attempt to bridge the representational gap for the domain of
model acquisition. Specifically, we address the problem of automatically
acquiring a generic 2-D view-based class model from a set of images,
each containing an exemplar object belonging to that class. We
introduce a novel graph-theoretical formulation of the problem, and
demonstrate the approach on real imagery.
This is joint work with Yakov Keselman, Rutgers University
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