Toward robust moment invariants for image registration

Nawaf I. Almoosa, Soo Hyun Bae, Biing Hwang Juang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

We apply pattern recognition techniques to enhance the robustness of moment-invariants-based image classifiers. Moment invariants exhibit variations under transformations that do not preserve the original image function, such as geometrical transformations involving interpolation. Such variations degrade the performance of classifiers due to the errors in the nearest neighbor search stage. We propose the use of Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) to alleviate the variations and enhance the robustness of classification. We demonstrate the improved performance in image registration applications under spatial scaling and rotation transformations.

Original languageBritish English
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages1009-1012
Number of pages4
DOIs
StatePublished - 2008
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: 31 Mar 20084 Apr 2008

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Country/TerritoryUnited States
CityLas Vegas, NV
Period31/03/084/04/08

Keywords

  • Image registration
  • Linear discriminant analysis
  • Moment invariants
  • Principle component analysis

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