DocumentCode :
112633
Title :
Parameter Estimation in Spherical Symmetry Groups
Author :
Yu-Hui Chen ; Wei, Dennis ; Newstadt, Gregory ; DeGraef, Marc ; Simmons, Jeffrey ; Hero, Alfred
Author_Institution :
Univ. of Michigan, Ann Arbor, MI, USA
Volume :
22
Issue :
8
fYear :
2015
fDate :
Aug. 2015
Firstpage :
1152
Lastpage :
1155
Abstract :
This letter considers statistical estimation problems where the probability distribution of the observed random variable is invariant with respect to actions of a finite topological group. It is shown that any such distribution must satisfy a restricted finite mixture representation. When specialized to the case of distributions over the sphere that are invariant to the actions of a finite spherical symmetry group G, a group-invariant extension of the Von Mises Fisher (VMF) distribution is obtained. The G-invariant VMF is parameterized by location and scale parameters that specify the distribution´s mean orientation and its concentration about the mean, respectively. Using the restricted finite mixture representation these parameters can be estimated using an Expectation Maximization (EM) maximum likelihood (ML) estimation algorithm. This is illustrated for the problem of mean crystal orientation estimation under the spherically symmetric group associated with the crystal form, e.g., cubic or octahedral or hexahedral. Simulations and experiments establish the advantages of the extended VMF EM-ML estimator for data acquired by Electron Backscatter Diffraction (EBSD) microscopy of a polycrystalline Nickel alloy sample.
Keywords :
crystal orientation; crystal symmetry; electron backscattering; electron diffraction; expectation-maximisation algorithm; maximum likelihood estimation; nickel alloys; parameter estimation; statistical analysis; statistical distributions; EBSD microscopy; EM ML estimation algorithm; G-invariant VMF; VMF EM-ML estimator; VMF distribution; Von Mises Fisher distribution; electron backscatter diffraction microscopy; expectation maximization maximum likelihood estimation algorithm; finite mixture representation; finite topological group; group-invariant extension; mean crystal orientation estimation; parameter estimation; polycrystalline nickel alloy sample; probability distribution; random variable distribution; spherical symmetry groups; statistical estimation problems; Crystals; Maximum likelihood estimation; Random variables; Signal processing algorithms; Vectors; Please add index terms;
fLanguage :
English
Journal_Title :
Signal Processing Letters, IEEE
Publisher :
ieee
ISSN :
1070-9908
Type :
jour
DOI :
10.1109/LSP.2014.2387206
Filename :
7001052
Link To Document :
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