Computing steerable principal components of a large set of images and their rotations

Colin Ponce, Amit Singer

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

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 languageEnglish (US)
Article number5759743
Pages (from-to)3051-3062
Number of pages12
JournalIEEE Transactions on Image Processing
Volume20
Issue number11
DOIs
StatePublished - Nov 2011

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design

Keywords

  • EDICS Category: TEC-PRC image and video processing techniques

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