Smoothness Prior Information in Principal Component Analysis of Dynamic Image Data
Principal component analysis is a well developed and under- stood method of multivariate data processing. Its optimal performance requires knowledge of noise covariance that is not available in most ap- plications. We suggest a method for estimation of noise covariance based on assumed smoothness of...
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| creator | Šmídl, Václav Kárný, Miroslav Šámal, Martin Backfrieder, Werner Szabo, Zsolt |
| description | Principal component analysis is a well developed and under- stood method of multivariate data processing. Its optimal performance requires knowledge of noise covariance that is not available in most ap- plications. We suggest a method for estimation of noise covariance based on assumed smoothness of the estimated dynamics. |
| doi_str_mv | 10.1007/3-540-45729-1_24 |
| format | Conference Proceeding |
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| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Information Processing in Medical Imaging, 2001, p.225-231 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_14045826 |
| source | Springer Books |
| subjects | Biological and medical sciences Dynamic Positron Emission Tomography Study Investigative techniques, diagnostic techniques (general aspects) Medical sciences Nervous system Noiseless Data Radiodiagnosis. Nmr imagery. Nmr spectrometry Scintillation Spectrum Serotonin Transporter Standard Principal Component Analysis |
| title | Smoothness Prior Information in Principal Component Analysis of Dynamic Image Data |
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