X-Y separable pyramid steerable scalable kernels

Author

Shy, Douglas ; Perona, Pietro

Author_Institution

California Inst. of Technol., Pasadena, CA, USA

fYear

1994

fDate

21-23 Jun 1994

Firstpage

237

Lastpage

244

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

Keywords

computer vision; edge detection; filtering and prediction theory; 3D singular value decomposition; Laplacian pyramid; X-Y separable pyramid steerable scalable kernels; convolution time; early vision edge-detection kernel; filter kernels; multi-resolution multi-orientation filtering; multi-way arrays; pseudo-SVD; storage requirements; Filtering; Image edge analysis; Image orientation analysis; Machine vision;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Vision and Pattern Recognition, 1994. Proceedings CVPR '94., 1994 IEEE Computer Society Conference on

Conference_Location

Seattle, WA

ISSN

1063-6919

Print_ISBN

0-8186-5825-8

Type

conf

DOI

10.1109/CVPR.1994.323835

Filename

323835