Inexact Bayesian point pattern matching for linear transformations
This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.
Reason for embargo
We introduce a novel Bayesian inexact point pattern matching model that assumes that a linear transformation relates the two sets of points. The matching problem is inexact due to the lack of one-to-one correspondence between the point sets and the presence of noise. The algorithm is itself inexact; we use variational Bayesian approximation to estimate the posterior distributions in the face of a problematic evidence term. The method turns out to be similar in structure to the iterative closest point algorithm.
This work was supported by the University of Exeter’s Bridging the Gaps initiative, which was funded by EPSRC award EP/I001433/1 and the collaboration was formed through the Exeter Imaging Network.
Vol. 47, Iss. 10, October 2014, pp. 3265 - 3275