Hypersphere Fitting From Noisy Data Using an EM Algorithm

This letter studies a new expectation maximization (EM) algorithm to solve the problem of circle, sphere and more generally hypersphere fitting. This algorithm relies on the introduction of random latent vectors having a priori independent von Mises-Fisher distributions defined on the hypersphere. T...

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Veröffentlicht in:	IEEE signal processing letters Jg. 28; S. 314 - 318
Hauptverfasser:	Lesouple, Julien, Pilastre, Barbara, Altmann, Yoann, Tourneret, Jean-Yves
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Schlagworte:	Algorithms Computer Science Distributed databases Engineering Sciences Expectation-Maximization Algorithm Fitting Hypersphere Fitting Hyperspheres Iterative algorithms Maximum likelihood estimation Noise measurement Signal and Image Processing Signal processing algorithms Statistical models Three-dimensional displays von Mises-Fisher distribution Maximum likelihood estimation von Mises-Fisher distribution Expectation-maximization algorithm Hypersphere fitting
ISSN:	1070-9908, 1558-2361
Online-Zugang:	Volltext
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