%0 Conference Paper
%B 2011 IEEE International Conference on Computer Vision (ICCV)
%D 2011
%T Blurring-invariant Riemannian metrics for comparing signals and images
%A Zhengwu Zhang
%A Klassen, E.
%A Srivastava, A.
%A Turaga,P.
%A Chellapa, Rama
%K blurring-invariant Riemannian metrics
%K Estimation
%K Fourier transforms
%K Gaussian blur function
%K Gaussian processes
%K image representation
%K log-Fourier representation
%K measurement
%K Orbits
%K Polynomials
%K signal representation
%K Space vehicles
%K vectors
%X We propose a novel Riemannian framework for comparing signals and images in a manner that is invariant to their levels of blur. This framework uses a log-Fourier representation of signals/images in which the set of all possible Gaussian blurs of a signal, i.e. its orbits under semigroup action of Gaussian blur functions, is a straight line. Using a set of Riemannian metrics under which the group actions are by isometries, the orbits are compared via distances between orbits. We demonstrate this framework using a number of experimental results involving 1D signals and 2D images.
%B 2011 IEEE International Conference on Computer Vision (ICCV)
%I IEEE
%P 1770 - 1775
%8 2011/11/06/13
%@ 978-1-4577-1101-5
%G eng
%R 10.1109/ICCV.2011.6126442