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Talk: Generalized Sparse MRF Appearance Models

Rene Donner

When Feb 19, 2009
from 03:00 pm to 03:00 pm
Where VrVis
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Abstract: Image segmentation or registration approaches that rely on a local search
paradigm (e.g, Active Appearance Models, Active Contours) require an
initialization that provides for considerable overlap or a coarse localization
of the object to be segmented or localized. We propose an approach that does
not need such an initialization, but localizes anatomical structures in a
global manner by formulating the localization task as the solution of a Markov
Random Field (MRF).

During search Sparse MRF Appearance Models (SAMs) relate a priori information
about the geometric configuration of landmarks and local appearance features
to a set of candidate points in the target image. They encode the
correspondence probabilities as an MRF, and the search in the target image is
equivalent to solving the MRF. The resulting node labels define a mapping of
the modeled object (e.g. a sequence of vertebrae) to the target image interest
points. The local appearance information is captured by novel symmetry-based
interest points and local descriptors derived from Gradient Vector Flow (GVF).
Alternatively, arbitrary interest points can be used. The approach does not
require initialization and finds the most plausible match of the query
structure in the entire image. It provides for precise, reliable and fast
localization of the structure.
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