Pampas: Real-Valued Graphical Models for Computer Vision
Michael Isard
Proc. Computer Vision and Pattern Recognition, vol. 1 613-620. (2003)
Abstract
Probabilistic models have been adopted for many computer vision
applications, however inference in high-dimensional spaces remains
problematic. As the state-space of a model grows, the dependencies
between the dimensions lead to an exponential growth in computation
when performing inference. Many common computer vision problems
naturally map onto the graphical model framework; the representation
is a graph where each node contains a portion of the state-space and
there is an edge between two nodes only if they are not independent
conditional on the other nodes in the graph. When this graph is
sparsely connected, belief propagation algorithms can turn an
exponential inference computation into one which is linear in the size
of the graph. However belief propagation is only applicable when the
variables in the nodes are discrete-valued or jointly represented by a
single multivariate Gaussian distribution, and this rules out many
computer vision applications.
This paper combines belief propagation with ideas from particle
filtering; the resulting algorithm performs inference on graphs
containing both cycles and continuous-valued latent variables with
general conditional probability distributions. Such graphical models
have wide applicability in the computer vision domain and we test the
algorithm on example problems of low-level edge linking and locating
jointed structures in clutter.
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