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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
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
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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. This statistical model leads to a complete data likelihood whose expected value, conditioned on the observed data, has a Von Mises-Fisher distribution. As a result, the inference problem can be solved with a simple EM algorithm. The performance of the resulting hypersphere fitting algorithm is evaluated for circle and sphere fitting.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2021.3051851