Blurring-invariant Riemannian metrics for comparing signals and images

Zhengwu Zhang, Eric Klassen, Anuj Srivastava, Pavan Turaga, Rama Chellappa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2011 International Conference on Computer Vision, ICCV 2011
Pages1770-1775
Number of pages6
DOIs
StatePublished - Dec 1 2011
Externally publishedYes
Event2011 IEEE International Conference on Computer Vision, ICCV 2011 - Barcelona, Spain
Duration: Nov 6 2011Nov 13 2011

Publication series

NameProceedings of the IEEE International Conference on Computer Vision

Other

Other2011 IEEE International Conference on Computer Vision, ICCV 2011
CountrySpain
CityBarcelona
Period11/6/1111/13/11

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ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Zhang, Z., Klassen, E., Srivastava, A., Turaga, P., & Chellappa, R. (2011). Blurring-invariant Riemannian metrics for comparing signals and images. In 2011 International Conference on Computer Vision, ICCV 2011 (pp. 1770-1775). [6126442] (Proceedings of the IEEE International Conference on Computer Vision). https://doi.org/10.1109/ICCV.2011.6126442