0410627 20240103182227.520060210235959.9 3-540-42245-5 New York Springer 2001 7 s. Lecture Notes in Computer Science. 2082 227-233InsanaM. F.LeahyR. M. PCA prior information dynamic medical imaging cav_un_auth*0101207 Šmídl Václav UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101124 Kárný Miroslav UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0211467 Šámal M. CZ cav_un_auth*0212056 Backfrieder W. AT cav_un_auth*0212806 Szabo Z. US http://library.utia.cas.cz/separaty/historie/karny-smoothness prior information in principal component analysis of dynamic image data.pdf 06Y GA102/99/1564 GA ČR cav_un_auth*0004444 NN5382 GA MZd cav_un_auth*0030355 AV0Z1075907 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. cav_un_auth*0212807 International Conference IPMI 2001 /17./ Davis US 17.06.2001-22.06.2001 BB AS http://hdl.handle.net/11104/0130716 UTIA-B 20010096 2001 2001 New York Springer 3-540-42245-5 Information Processing in Medical Imaging 227 233 Insana M. F. 340 Leahy R. M. 340