Steerable Principal Components for Space-Frequency Localized Images

  title={Steerable Principal Components for Space-Frequency Localized Images},
  author={Boris Landa and Yoel Shkolnisky},
  journal={SIAM journal on imaging sciences},
  volume={10 2},
As modern scientific image datasets typically consist of a large number of images of high resolution, devising methods for their accurate and efficient processing is a central research task. In this paper, we consider the problem of obtaining the steerable principal components of a dataset, a procedure termed "steerable PCA" (steerable principal component analysis). The output of the procedure is the set of orthonormal basis functions which best approximate the images in the dataset and all of… CONTINUE READING
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