|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
BAYESIAN REGISTRATION OF MODELS USING FEM EIGENMODES
Mike Syn and Richard Prager
Highest Confidence First (HCF) estimation is applied to deterministic scaled-ordered non-rigid registration of organ models. A local Posterior energy measure is computed from Bayesian combination of local Prior and Likelihood energy measures, over a Markov Random Field (MRF) defined over the Finite Element neighbourhood of every element node. Prior energy is derived from the Gompertz metric of biological growth, and Likelihood energy is derived from the biologically meaningful similarity between local FEM eigenmode displacement components. The Centroid Size metric is generalised to give the characteristic scale of an organ model, which allows for normalisation of model size and eigenmode magnitude. Linear axes along which modal moments act are used as an estimate of intrinsic model pose, so that initial rigid-body registration can be achieved.
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2005 Cambridge University Engineering Dept
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