DocumentCode :
1411273
Title :
Robust Point Set Registration Using Gaussian Mixture Models
Author :
Jian, Bing ; Vemuri, Baba C.
Author_Institution :
Siemens Med. Solutions, Malvern, PA, USA
Volume :
33
Issue :
8
fYear :
2011
Firstpage :
1633
Lastpage :
1645
Abstract :
In this paper, we present a unified framework for the rigid and nonrigid point set registration problem in the presence of significant amounts of noise and outliers. The key idea of this registration framework is to represent the input point sets using Gaussian mixture models. Then, the problem of point set registration is reformulated as the problem of aligning two Gaussian mixtures such that a statistical discrepancy measure between the two corresponding mixtures is minimized. We show that the popular iterative closest point (ICP) method and several existing point set registration methods in the field are closely related and can be reinterpreted meaningfully in our general framework. Our instantiation of this general framework is based on the the L2 distance between two Gaussian mixtures, which has the closed-form expression and in turn leads to a computationally efficient registration algorithm. The resulting registration algorithm exhibits inherent statistical robustness, has an intuitive interpretation, and is simple to implement. We also provide theoretical and experimental comparisons with other robust methods for point set registration.
Keywords :
Gaussian processes; image registration; iterative methods; statistical analysis; Gaussian mixture models; iterative closest point method; robust point set registration problem; statistical robustness; Closed-form solution; Correlation; Iterative closest point algorithm; Kernel; Maximum likelihood estimation; Pattern matching; Robustness; Gaussian mixtures; Point set registration; nonrigid registration; robust matching.;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/TPAMI.2010.223
Filename :
5674050
Link To Document :
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