Steerable Principal Components for Space-Frequency Localized Images

@article{Landa2017SteerablePC,
  title={Steerable Principal Components for Space-Frequency Localized Images},
  author={Boris Landa and Yoel Shkolnisky},
  journal={SIAM journal on imaging sciences},
  year={2017},
  volume={10 2},
  pages={508-534}
}
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
Recent Discussions
This paper has been referenced on Twitter 19 times over the past 90 days. VIEW TWEETS

References

Publications referenced by this paper.
Showing 1-10 of 37 references

Approximation scheme for essentially bandlimited and space-concentrated functions on a disk

  • Boris Landa, Yoel Shkolnisky
  • Applied and Computational Harmonic Analysis,
  • 2016
8 Excerpts

On generalized prolate spheroidal functions

  • Kirill Serkh
  • Technical Report TR-1519,
  • 2015
1 Excerpt

Similar Papers

Loading similar papers…