Zernike moments have many desirable properties, such as rotation invariance,
robustness to noise, expression efficiency, fast computation and
multi-level representation for describing the shapes of patterns,
but there is a major drawback with Zernike moments: they need to
normalize an image to achieve scale invariance. This introduces some
errors since it involves the re-sampling and re-quantifying of digital
images, and leads to inaccuracy of classifier. In this paper, we
present improved Zernike moments, with theory and experiments that
show that the improved Zernike moments not only have better rotation
invariance, but also have scale invariance. Invariance of the improved
Zernike moments shows great improvement over previous methods.
%0 Journal Article
%1 Bin2002
%A Bin, Ye
%A Jia-Xiong, Peng
%D 2002
%J Journal of Optics A: Pure and Applied Optics
%K Zernike geometric image invariance, moments, rotation scale trademark translation
%N 6
%P 606--614
%R doi:10.1088/1464-4258/4/6/304
%T Invariance analysis of improved Zernike moments
%U http://www.iop.org/EJ/abstract/1464-4258/4/6/304
%V 4
%X Zernike moments have many desirable properties, such as rotation invariance,
robustness to noise, expression efficiency, fast computation and
multi-level representation for describing the shapes of patterns,
but there is a major drawback with Zernike moments: they need to
normalize an image to achieve scale invariance. This introduces some
errors since it involves the re-sampling and re-quantifying of digital
images, and leads to inaccuracy of classifier. In this paper, we
present improved Zernike moments, with theory and experiments that
show that the improved Zernike moments not only have better rotation
invariance, but also have scale invariance. Invariance of the improved
Zernike moments shows great improvement over previous methods.
@article{Bin2002,
abstract = {Zernike moments have many desirable properties, such as rotation invariance,
robustness to noise, expression efficiency, fast computation and
multi-level representation for describing the shapes of patterns,
but there is a major drawback with Zernike moments: they need to
normalize an image to achieve scale invariance. This introduces some
errors since it involves the re-sampling and re-quantifying of digital
images, and leads to inaccuracy of classifier. In this paper, we
present improved Zernike moments, with theory and experiments that
show that the improved Zernike moments not only have better rotation
invariance, but also have scale invariance. Invariance of the improved
Zernike moments shows great improvement over previous methods.},
added-at = {2011-03-27T19:47:06.000+0200},
author = {Bin, Ye and Jia-Xiong, Peng},
biburl = {https://www.bibsonomy.org/bibtex/21c09ca6d0eed53945a4fdddf5cf861cb/cocus},
day = 20,
doi = {doi:10.1088/1464-4258/4/6/304},
file = {:./oa2604.pdf:PDF},
interhash = {0a10ffdbd87ca5f7cf1c63175c094fe1},
intrahash = {1c09ca6d0eed53945a4fdddf5cf861cb},
journal = {Journal of Optics A: Pure and Applied Optics},
keywords = {Zernike geometric image invariance, moments, rotation scale trademark translation},
month = sep,
number = 6,
owner = {CK},
pages = {606--614},
timestamp = {2011-03-27T19:47:06.000+0200},
title = {Invariance analysis of improved Zernike moments},
url = {http://www.iop.org/EJ/abstract/1464-4258/4/6/304},
urldate = {2008.02.25},
volume = 4,
year = 2002
}