Computing steerable principal components of a large set of images and their rotations
We present here an efficient algorithm to compute the Principal Component Analysis (PCA) of a large image set consisting of images and, for each image, the set of its uniform rotations in the plane. We do this by pointing out the block circulant structure of the covariance matrix and utilizing that...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 20(2011), 11 vom: 01. Nov., Seite 3051-62 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
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2011
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article Research Support, N.I.H., Extramural |
| Online verfügbar |
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