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Abstract:
Our aim in this paper is to develop a Bayesian framework for
matching hierarchical relational models. Such models are widespread
in computer vision. The framework that we adopt for this study is
provided by iterative discrete relaxation. Here the aim is to
assign the discrete matches so as to optimise a global cost
function that draws information concerning the consistency of match
from different levels of the hierarchy. Our Bayesian development
naturally distinguishes between intra-level and inter-level
constraints. This allows the impact of reassigning a match to be
assessed not only at its own (or peer) level of representation, but
also upon its parents and children in the hierarchy. We illustrate
the effectiveness of the technique in the matching of line-segment
groupings in SAR images of rural scenes.
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