The problem of rotation-, scale-, and translation-invariant recognition
of images is discussed. A set of rotation-invariant features are
introduced. They are the magnitudes of a set of orthogonal complex
moments of the image known as Zernike moments. Scale and translation
invariance are obtained by first normalizing the image with respect
to these parameters using its regular geometrical moments. A systematic
reconstruction-based method for deciding the highest-order Zernike
moments required in a classification problem is developed. The quality
of the reconstructed image is examined through its comparison to
the original one. The orthogonality property of the Zernike moments,
which simplifies the process of image reconstruction, make the suggest
feature selection approach practical. Features of each order can
also be weighted according to their contribution to the reconstruction
process. The superiority of Zernike moment features over regular
moments and moment invariants was experimentally verified
%0 Journal Article
%1 Khotanzad1990
%A Khotanzad, A.
%A Hong, Y.H.
%D 1990
%K feature features, geometrical image invariance invariance, invariant moments, orthogonality, pattern picture processingZernike recognition, reconstruction, rotation-invariant scale selection, translation
%N 5
%P 489-497
%R 10.1109/34.55109
%T Invariant image recognition by Zernike moments
%V 12
%X The problem of rotation-, scale-, and translation-invariant recognition
of images is discussed. A set of rotation-invariant features are
introduced. They are the magnitudes of a set of orthogonal complex
moments of the image known as Zernike moments. Scale and translation
invariance are obtained by first normalizing the image with respect
to these parameters using its regular geometrical moments. A systematic
reconstruction-based method for deciding the highest-order Zernike
moments required in a classification problem is developed. The quality
of the reconstructed image is examined through its comparison to
the original one. The orthogonality property of the Zernike moments,
which simplifies the process of image reconstruction, make the suggest
feature selection approach practical. Features of each order can
also be weighted according to their contribution to the reconstruction
process. The superiority of Zernike moment features over regular
moments and moment invariants was experimentally verified
@article{Khotanzad1990,
abstract = {The problem of rotation-, scale-, and translation-invariant recognition
of images is discussed. A set of rotation-invariant features are
introduced. They are the magnitudes of a set of orthogonal complex
moments of the image known as Zernike moments. Scale and translation
invariance are obtained by first normalizing the image with respect
to these parameters using its regular geometrical moments. A systematic
reconstruction-based method for deciding the highest-order Zernike
moments required in a classification problem is developed. The quality
of the reconstructed image is examined through its comparison to
the original one. The orthogonality property of the Zernike moments,
which simplifies the process of image reconstruction, make the suggest
feature selection approach practical. Features of each order can
also be weighted according to their contribution to the reconstruction
process. The superiority of Zernike moment features over regular
moments and moment invariants was experimentally verified},
added-at = {2011-03-27T19:35:34.000+0200},
author = {Khotanzad, A. and Hong, Y.H.},
biburl = {https://www.bibsonomy.org/bibtex/20e1d01b1cf99ac452e8a50d8fe3b8fc6/cocus},
doi = {10.1109/34.55109},
file = {:./00055109.pdf:PDF},
interhash = {373bdd36d6a7493695c1a8acc2b60f3f},
intrahash = {0e1d01b1cf99ac452e8a50d8fe3b8fc6},
issn = {0162-8828},
journaltitle = {#ieeetpami#},
keywords = {feature features, geometrical image invariance invariance, invariant moments, orthogonality, pattern picture processingZernike recognition, reconstruction, rotation-invariant scale selection, translation},
month = may,
number = 5,
pages = {489-497},
timestamp = {2011-03-27T19:35:40.000+0200},
title = {Invariant image recognition by Zernike moments},
volume = 12,
year = 1990
}