MULTIPLE GRAPH MATCHING WITH BAYESIAN-INFERENCE

This paper describes the development of a Bayesian framework for multi ple graph matching. The study is motivated by the plethora of multi-se nsor fusion problems which can be abstracted as multiple graph matchin g tasks. The study uses as its starting point the Bayesian consistency measure recently developed by Wilson and Hancock. Hitherto, the consi stency measure has been used exclusively in the matching of graph-pair s. In the multiple graph matching study reported in this paper, we use the Bayesian framework to construct an inference matrix which can be used to gauge the mutual consistency of multiple graph-matches. The mu ltiple graph-matching process is realised as an iterative discrete rel axation process which aims to maximise the elements of the inference m atrix. We experiment with our multiple graph matching process using an application vehicle furnished by the matching of aerial imagery. Here we are concerned with the simultaneous fusion of optical, infra-red a nd synthetic aperture radar images in the presence of digital map data . (C) 1997 Elsevier Science B.V.