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 structure to compute its eigenvectors. We also demonstrate the advantages of this algorithm over similar ones with numerical experiments. Although it is useful in many settings, we illustrate the specific application of the algorithm to the problem of cryo-electron microscopy.
|Original language||English (US)|
|Number of pages||12|
|Journal||IEEE Transactions on Image Processing|
|State||Published - Nov 2011|
All Science Journal Classification (ASJC) codes
- Computer Graphics and Computer-Aided Design
- EDICS Category: TEC-PRC image and video processing techniques