Inexact Bayesian point pattern matching for linear transformations

PATTERN RECOGNITION

卷:47

Article

Christmas, J.¹  Everson, R. M.¹  Bell, J.²  Winlove, C. P.³ 

[1] Univ Exeter, Dept Comp Sci, Exeter EX4 4QF, Devon, England

[2] Univ Exeter, Peninsula Coll Med & Dent, Exeter EX1 2LU, Devon, England

[3] Univ Exeter, Dept Biophys, Exeter EX4 4QF, Devon, England

关键词: Bayesian methods;    Variational approximation;    Point pattern matching;    Iterative closest point algorithm;    Linear transformation;   

DOI  :  10.1016/j.patcog.2014.04.022

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

Free