ML Anthology

X-Y Separable Pyramid Steerable Scalable Kernels

Douglas Shy, Pietro Perona
CVPR 1994 pp. 237-244
doi:10.1109/CVPR.1994.323835 /cvpr/1994/shy1994cvpr-x/

Abstract

A new method for generating x-y separable steerable scalable approximations offilter kemels is proposed which is based on a generalization of the Singular Value De-composition (SVD) to 3 dimensions. This “pseudo-SVD” impmves upon apwwus scheme due to Pemna in that it reduces convolution time and storage requirements. An adaptation of the pseudo-SVD is proposed to generate steerable and scalable kemels which an? suitable for use with a Laplacian pyramid. The properties of this method are illustrated experimentally in generating steerable and scalable approximations to an early vision edge-detection kernel.

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Cite

Text

Shy and Perona. "X-Y Separable Pyramid Steerable Scalable Kernels." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1994. doi:10.1109/CVPR.1994.323835

Markdown

[Shy and Perona. "X-Y Separable Pyramid Steerable Scalable Kernels." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1994.](https://mlanthology.org/cvpr/1994/shy1994cvpr-x/) doi:10.1109/CVPR.1994.323835

BibTeX

@inproceedings{shy1994cvpr-x,
  title     = {{X-Y Separable Pyramid Steerable Scalable Kernels}},
  author    = {Shy, Douglas and Perona, Pietro},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1994},
  pages     = {237-244},
  doi       = {10.1109/CVPR.1994.323835},
  url       = {https://mlanthology.org/cvpr/1994/shy1994cvpr-x/}
}