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Inexact Bayesian point pattern matching for linear transformations
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DOI：10.1016/j.patcog.2014.04.022.png)
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Abstract
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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-toone 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. (C) 2014 Elsevier Ltd. All rights reserved.
Keywords:
Bayesian methods
Variational approximation
Point pattern matching
Iterative closest point algorithm
Linear transformation
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