Accurate and Efficient Approximation of the Continuous Gaussian Scale-Space
U. Köthe. Pattern Recognition, Proc. DAGM '04, volume 3175 of Springer LNCS, page 350-358. Springer, (2004)
Abstract
The Gaussian scale-space is a standard tool in image analysis. While continuous in theory, it is generally realized with fixed regular grids in practice. This prevents the use of algorithms which require continuous and differentiable data and adaptive step size control, such as numerical path following. We propose an efficient continuous approximation of the Gaussian scale-space that removes this restriction and opens up new ways to subpixel feature detection and scale adaptation.
%0 Conference Paper
%1 koethe_04_AccurateandEfficient
%A Köthe, Ullrich
%B Pattern Recognition, Proc. DAGM '04
%D 2004
%E Rasmussen, C.E.
%E Bülthoff, H.
%E Giese, M.
%E Schölkopf, B.
%I Springer
%K continuous gaussian scale-space approximation
%P 350-358
%T Accurate and Efficient Approximation of the Continuous Gaussian Scale-Space
%U http://www.springerlink.com/content/0cln87j2pyfb6jxt
%V 3175
%X The Gaussian scale-space is a standard tool in image analysis. While continuous in theory, it is generally realized with fixed regular grids in practice. This prevents the use of algorithms which require continuous and differentiable data and adaptive step size control, such as numerical path following. We propose an efficient continuous approximation of the Gaussian scale-space that removes this restriction and opens up new ways to subpixel feature detection and scale adaptation.
@inproceedings{koethe_04_AccurateandEfficient,
abstract = {The Gaussian scale-space is a standard tool in image analysis. While continuous in theory, it is generally realized with fixed regular grids in practice. This prevents the use of algorithms which require continuous and differentiable data and adaptive step size control, such as numerical path following. We propose an efficient continuous approximation of the Gaussian scale-space that removes this restriction and opens up new ways to subpixel feature detection and scale adaptation.},
added-at = {2010-05-20T05:34:01.000+0200},
author = {Köthe, Ullrich},
biburl = {https://www.bibsonomy.org/bibtex/2077c0c673a7c73b24a6d5399e96a4aa1/ukoethe},
booktitle = {Pattern Recognition, Proc. DAGM '04},
editor = {Rasmussen, C.E. and Bülthoff, H. and Giese, M. and Schölkopf, B.},
file = {:continuousScaleSpace.pdf:PDF},
howpublished = {http://www.springerlink.com/content/0cln87j2pyfb6jxt},
interhash = {b8a7fe4dc0f0e8ac6fdda618703cf8f3},
intrahash = {077c0c673a7c73b24a6d5399e96a4aa1},
keywords = {continuous gaussian scale-space approximation},
pages = {350-358},
publisher = {Springer},
series = {Springer LNCS},
timestamp = {2010-05-20T05:34:01.000+0200},
title = {Accurate and Efficient Approximation of the Continuous Gaussian {Scale-Space}},
url = {http://www.springerlink.com/content/0cln87j2pyfb6jxt},
volume = 3175,
year = 2004
}