2 research outputs found
Factor Graphs for Computer Vision and Image Processing
Factor graphs have been used extensively in the decoding of error
correcting codes such as turbo codes, and in signal processing.
However, while computer vision and pattern recognition are awash
with graphical model usage, it is some-what surprising that
factor graphs are still somewhat under-researched in these
communities. This is surprising because factor graphs naturally
generalise both Markov random fields and Bayesian networks.
Moreover, they are useful in modelling relationships between
variables that are not necessarily probabilistic and allow for
efficient marginalisation via a sum-product of probabilities.
In this thesis, we present and illustrate the utility of factor
graphs in the vision community through some of the field’s
popular problems. The thesis does so with a particular focus on
maximum a posteriori (MAP) inference in graphical
structures with layers. To this end, we are able to break-down
complex problems into factored representations and more
computationally realisable constructions. Firstly, we present a
sum-product framework that uses the explicit factorisation
in local subgraphs from the partitioned factor graph of a layered
structure to perform inference. This provides an efficient method
to perform inference since exact inference is attainable in the
resulting local subtrees. Secondly, we extend this framework to
the entire graphical structure without partitioning, and discuss
preliminary ways to combine outputs from a multilevel
construction. Lastly, we further our endeavour to combine
evidence from different methods through
a simplicial spanning tree reparameterisation of the factor graph
in a way that ensures consistency, to produce an ensembled and
improved result. Throughout the thesis, the underlying feature we
make use of is to enforce adjacency constraints using Delaunay
triangulations computed by adding points dynamically, or using a
convex hull algorithm. The adjacency relationships from Delaunay
triangulations aid the factor graph approaches in this thesis to
be both efficient and
competitive for computer vision tasks. This is because of the low
treewidth they provide in local subgraphs, as well as the
reparameterised interpretation of the graph they form through the
spanning tree of simplexes. While exact inference is known to be
intractable for junction trees obtained from the loopy graphs in
computer vision, in this thesis we are able to effect exact
inference on our spanning tree of simplexes. More importantly,
the approaches presented here are not restricted to the computer
vision and image processing fields, but are extendable to more
general applications that involve distributed computations