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. https://resolver.caltech.edu/CaltechAUTHORS:SHYcvpr94
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Abstract
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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| 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 | ||||||
| DOI: | 10.1109/CVPR.1994.323835 | ||||||
| Record Number: | CaltechAUTHORS:SHYcvpr94 | ||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:SHYcvpr94 | ||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||
| ID Code: | 3438 | ||||||
| Collection: | CaltechAUTHORS | ||||||
| Deposited By: | Tony Diaz | ||||||
| Deposited On: | 07 Jun 2006 | ||||||
| Last Modified: | 08 Nov 2021 19:56 |
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