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X-Y separable pyramid steerable scalable kernels

Shy, Douglas and Perona, Pietro (1994) X-Y separable pyramid steerable scalable kernels. In: Proceedings, 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition: June 21-23, 1994, Seattle, Washington. IEEE , Los Alamitos, CA, pp. 237-244.

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A new method for generating X-Y separable, steerable, scalable approximations of filter kernels is proposed which is based on a generalization of the singular value decomposition (SVD) to three dimensions. This “pseudo-SVD” improves upon a previous scheme due to Perona (1992) in that it reduces convolution time and storage requirements. An adaptation of the pseudo-SVD is proposed to generate steerable and scalable kernels which are 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.

Item Type:Book Section
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Perona, Pietro0000-0002-7583-5809
Additional Information:© Copyright 1994 IEEE. The authors wish to acknowledge the following people: Mike Burl for his suggestions and work regarding the pyramid scheme; Thomas Leung for debugging the matlab code and helping with implementation; Jan de Leeuw and Steve Breiner for pointing out previous work done on 3D tensor decomposition.
Subject Keywords:steerable; scalable filters; multi-resolution; SVD; pyramid; multi-orientation filtering; early vision; multi-way arrays
Record Number:CaltechAUTHORS:SHYcvpr94
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:3438
Deposited By: Tony Diaz
Deposited On:07 Jun 2006
Last Modified:08 Nov 2021 19:56

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