PROCEEDINGS IPMU '08
Grammatical Inference by n-gram Modeling of Convex Groups: Representation of Visual Random Polytopes
Peter Michael Goebel, Markus Vincze, Bernard Favre-Bulle.
In this paper, a joint solution to
the problem of finding appropriate
abstract representations for visual
polytopes is given. By using support
from convex and stochastic geometry,
collecting information of views
from different viewpoints, perceptual
grouping of 3D point-cloud image
points into halfplanes with probabilistic
robust fitting and the segmentation
of edges and corners by
intersecting halfplanes yields an aggregation
of visual primitives into
object prototypes by Bayes’ belief
networks. In order to build object
prototypes, a n-gram model is
trained by edge and corner primitives,
derived from Monte-Carlo simulations
and processing of real 3D
point-clouds. Finally, we use perplexity
to find out the best performing
network and define a Dirichlet
distribution model of the n-grams.
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