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Smoothness prior information in principal component analysis of dynamic image data
- 1.0410627 - UTIA-B 20010096 RIV US eng C - Conference Paper (international conference)
Šmídl, Václav - Kárný, Miroslav - Šámal, M. - Backfrieder, W. - Szabo, Z.
Smoothness prior information in principal component analysis of dynamic image data.
New York: Springer, 2001. Lecture Notes in Computer Science., 2082. ISBN 3-540-42245-5. In: Information Processing in Medical Imaging. - (Insana, M.; Leahy, R.), s. 227-233
[International Conference IPMI 2001 /17./. Davis (US), 17.06.2001-22.06.2001]
R&D Projects: GA ČR GA102/99/1564; GA MZd NN5382
Institutional research plan: AV0Z1075907
Keywords : PCA * prior information * dynamic medical imaging
Subject RIV: BB - Applied Statistics, Operational Research
http://library.utia.cas.cz/separaty/historie/karny-smoothness prior information in principal component analysis of dynamic image data.pdf
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.
Permanent Link: http://hdl.handle.net/11104/0130716
Number of the records: 1