Title :
Steerable kernels for arbitrarily-sampled spaces
Author :
Benoit, Stephen ; Ferrie, Frank P.
Author_Institution :
Center for Intelligent Mach., McGill Univ., Montreal, Que., Canada
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;
Conference_Titel :
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
Print_ISBN :
0-7695-2128-2
DOI :
10.1109/ICPR.2004.1334595
