DocumentCode
419815
Title
Steerable kernels for arbitrarily-sampled spaces
Author
Benoit, Stephen ; Ferrie, Frank P.
Author_Institution
Center for Intelligent Mach., McGill Univ., Montreal, Que., Canada
Volume
3
fYear
2004
fDate
23-26 Aug. 2004
Firstpage
578
Abstract
This paper describes a procedure for generating steerable kernels corresponding to general image mappings (e.g., non-rigid transformations) for arbitrary image tessellations. The paper presents the basic components that can efficiently rotate functions over arbitrary samplings of an image space, including rectilinear grids and irregularly - spaced pixels.
Keywords
approximation theory; computational geometry; image sampling; singular value decomposition; approximation theory; arbitrarily sampled spaces; arbitrary image tesselations; computational geometry; image mappings; image pixels; image space sampling; singular value decomposition; steerable kernel generation; Character generation; Computational efficiency; Frequency; Image sampling; Interpolation; Kernel; Machine intelligence; Matrix decomposition; Nonlinear filters; Pixel;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
ISSN
1051-4651
Print_ISBN
0-7695-2128-2
Type
conf
DOI
10.1109/ICPR.2004.1334595
Filename
1334595
Link To Document