Smoothness prior information in principal component analysis of dynamic image data

Václav Šmídl, Miroslav Kárný, Martin Šámal, Werner Backfrieder, Zsolt Szabo

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationInformation Processing in Medical Imaging - 17th International Conference, IPMI 2001, Proceedings
EditorsMichael F. Insana, Richard M. Leahy
PublisherSpringer
Pages225-231
Number of pages7
ISBN (Electronic)3540422455, 9783540422457
DOIs
Publication statusPublished - 2001
Externally publishedYes
Event17th International Conference on Information Processing in Medical Imaging, IPMI 2001 - Davis, United States
Duration: 18 Jun 200122 Jun 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2082
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Information Processing in Medical Imaging, IPMI 2001
Country/TerritoryUnited States
CityDavis
Period18.06.200122.06.2001

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