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 proceedingConference contribution

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 (US)
Title of host publicationInformation Processing in Medical Imaging - 17th International Conference, IPMI 2001, Proceedings
EditorsRichard M. Leahy, Michael F. Insana
PublisherSpringer Verlag
Pages225-231
Number of pages7
ISBN (Electronic)3540422455, 9783540422457
StatePublished - Jan 1 2001
Event17th International Conference on Information Processing in Medical Imaging, IPMI 2001 - Davis, United States
Duration: Jun 18 2001Jun 22 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

Other

Other17th International Conference on Information Processing in Medical Imaging, IPMI 2001
CountryUnited States
CityDavis
Period6/18/016/22/01

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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