Inexact Bayesian point pattern matching for linear transformations
Christmas, JT; Everson, RM; Bell, J; et al.Winlove, CP
Date: 6 May 2014
Article
Journal
Pattern Recognition
Publisher
Elsevier
Publisher DOI
Abstract
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 ...
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.
Computer Science
Faculty of Environment, Science and Economy
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