Smoothness prior information in principal component analysis of dynamic image data

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SYSNO ASEP	0410627
Document Type	C - Proceedings Paper (int. conf.)
R&D Document Type	Conference Paper
Title	Smoothness prior information in principal component analysis of dynamic image data
Author(s)	Šmídl, Václav (UTIA-B)_{RID, ORCID} Kárný, Miroslav (UTIA-B)_{RID, ORCID} Šámal, M. (CZ) Backfrieder, W. (AT) Szabo, Z. (US)
Issue data	New York: Springer, 2001
ISBN	3-540-42245-5
Source Title	Information Processing in Medical Imaging / Insana M. F. ; Leahy R. M.
Pages	s. 227-233
Series	Lecture Notes in Computer Science.
Series number	2082
Number of pages	7 s.
Action	International Conference IPMI 2001 /17./
Event date	17.06.2001-22.06.2001
VEvent location	Davis
Country	US - United States
Event type	WRD
Language	eng - English
Country	US - United States
Keywords	PCA ; prior information ; dynamic medical imaging
Subject RIV	BB - Applied Statistics, Operational Research
R&D Projects	GA102/99/1564 GA ČR - Czech Science Foundation (CSF)
	NN5382 GA MZd - Ministry of Health (MZ)
CEZ	1075907
Annotation	Principal component analysis is a well developed and understood method of multivariate data processing. Its optimal performance requires knowledge of noise covariance that is not available in most applications. We suggest a method for estimation of noise covariance based on assumed smoothness of the estimated dynamics.
Workplace	Institute of Information Theory and Automation
Contact	Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201.

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