Open AccessVariational Bayesian Approximation for Affine Point Set Matching
Author(s) -
Xiang Lin,
Qu Hanbing,
Tao Haijun,
Wang Jiaqiang
Publication year - 2015
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2015.04.021
Subject(s) - spurious relationship , affine transformation , outlier , algorithm , gaussian , mathematics , robustness (evolution) , affine hull , affine combination , bayesian probability , posterior probability , directed acyclic graph , point set registration , computer science , random variable , harris affine region detector , transformation (genetics) , probabilistic logic , matching (statistics) , mathematical optimization , artificial intelligence , point (geometry) , affine shape adaptation , affine space , statistics , biochemistry , physics , chemistry , geometry , quantum mechanics , pure mathematics , gene
In this paper, we propose a variational approach for the affine point set matching problems under the Bayesian probabilistic framework. A directed acyclic graphis provided for the representation of the joint probability over affine transformation, random variables and the point sets. Based on the directed graph, a variational iterative algorithm is derived to approximate the posteriors of the random variables and the anisotropic Gaussian mixtures are used for the estimation of the spurious outliers instead of the frequently‐used uniform distribution. Experimental results demonstrate that our method achieves good performance in terms of both robustness and accuracy and is comparable to other state‐of‐the‐art point registration algorithms especially in the case of complicated outliers.