NDLI: Variational Bayesian Approximation for Affine Point Set Matching

Content Provider	IET Digital Library
Author	Xiang, Lin Qu, Hanbing Tao, Haijun Wang, Jiaqiang
Abstract	In this paper, we propose a variational approach for the affine point set matching problems under the Bayesian probabilistic framework. A directed acyclic graph is 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.
Starting Page	349
Ending Page	354
Page Count	6
ISSN	10224653
Volume Number	24
e-ISSN	20755597
Issue Number	Issue 2, Apr (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/24/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2015.04.021
Journal	Chinese Journal of Electronics
Publisher Date	2015-04-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	Affine Point Set Matching Affine Point Set Matching Problem Affine Transformation Anisotropic Gaussian Mixtures Approximation Theory Bayes Method Bayesian Probabilistic Framework Combinatorial Mathematics Directed Acyclic Graph Directed Graph Frequently-used Uniform Distribution Gaussian Processes Interpolation And Function Approximation Joint Probability Mixture Model Numerical Analysis Pattern Matching Pattern Recognition Random Variables Statistics Variational Bayesian Approximation Variational Iterative Algorithm
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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