Smoothness Prior Information in Principal Component Analysis of Dynamic Image Data

Principal component analysis is a well developed and under- stood method of multivariate data processing. Its optimal performance requires knowledge of noise covariance that is not available in most ap- plications. We suggest a method for estimation of noise covariance based on assumed smoothness of...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Šmídl, Václav, Kárný, Miroslav, Šámal, Martin, Backfrieder, Werner, Szabo, Zsolt
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Biological and medical sciences Dynamic Positron Emission Tomography Study Investigative techniques, diagnostic techniques (general aspects) Medical sciences Nervous system Noiseless Data Radiodiagnosis. Nmr imagery. Nmr spectrometry Scintillation Spectrum Serotonin Transporter Standard Principal Component Analysis
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	Principal component analysis is a well developed and under- stood method of multivariate data processing. Its optimal performance requires knowledge of noise covariance that is not available in most ap- plications. We suggest a method for estimation of noise covariance based on assumed smoothness of the estimated dynamics.
ISSN:	0302-9743 1611-3349
DOI:	10.1007/3-540-45729-1_24