Pseudo-Zernike polynomials

In mathematics, pseudo-Zernike polynomials are well known and widely used in the analysis of optical systems. They are also widely used in image analysis as shape descriptors.

Definition

They are an orthogonal set of complex-valued polynomials defined as

$V_{nm}(x,y) = R_{nm}(x,y)e^{jm\arctan(\frac{y}{x})},$

where $x^2+y^2\leq 1, n\geq 0, |m|\leq n$ and orthogonality on the unit disk is given as

$\int_0^{2\pi}\int_0^1 r [V_{nl}(r\cos\theta,r\sin\theta)]^* \times V_{mk}(r\cos\theta,r\sin\theta)\,dr\,d\theta = \frac{\pi}{n+1}\delta_{mn}\delta_{kl},$

where the star means complex conjugation, and $r^2 = x^2+y^2$, $x=r\cos\theta$, $y=r\sin\theta$ are the standard transformations between polar and Cartesian coordinates.

The radial polynomials $R_{nm}$ are defined as^[1]

$R_{nm}(x,y) = \sum_{s=0}^{n-|m|}D_{n,|m|,s}(x^2+y^2)^{(n-s)/2}$

with integer coefficients

$D_{n,m,s} = (-1)^s\frac{(2n+1-s)!}{s!(n-m-s)!(n+m-s+1)!}.$

Examples

Examples are:

$R_{0,0} = 1$

$R_{1,0} = -2+3 r$

$R_{1,1} = r$

$R_{2,0} = 3+10 r^2-12 r$

$R_{2,1} = 5 r^2-4 r$

$R_{2,2} = r^2$

$R_{3,0} = -4+35 r^3-60 r^2+30 r$

$R_{3,1} = 21 r^3-30 r^2+10 r$

$R_{3,2} = 7 r^3-6 r^2$

$R_{3,3} = r^3$

$R_{4,0} = 5+126 r^4-280 r^3+210 r^2-60 r$

$R_{4,1} = 84 r^4-168 r^3+105 r^2-20 r$

$R_{4,2} = 36 r^4-56 r^3+21 r^2$

$R_{4,3} = 9 r^4-8 r^3$

$R_{4,4} = r^4$

$R_{5,0} = -6+462 r^5-1260 r^4+1260 r^3-560 r^2+105 r$

$R_{5,1} = 330 r^5-840 r^4+756 r^3-280 r^2+35 r$

$R_{5,2} = 165 r^5-360 r^4+252 r^3-56 r^2$

$R_{5,3} = 55 r^5-90 r^4+36 r^3$

$R_{5,4} = 11 r^5-10 r^4$

$R_{5,5} = r^5$

Moments

The pseudo-Zernike Moments (PZM) of order $n$ and repetition $l$ are defined as

$A_{nl}=\frac{n+1}{\pi}\int_0^{2\pi}\int_0^1 [V_{nl}(r\cos\theta,r\sin\theta)]^* f(r\cos\theta,r\sin\theta)r\,dr\,d\theta,$

where $n = 0, \ldots \infty$, and $l$ takes on positive and negative integer values subject to $|l|\leq n$.

The image function can be reconstructed by expansion of the pseudo-Zernike coefficients on the unit disk as

$f(x,y) = \sum_{n=0}^{\infty}\sum_{l=-n}^{+n}A_{nl}V_{nl}(x,y).$

Pseudo-Zernike moments are derived from conventional Zernike moments and shown to be more robust and less sensitive to image noise than the Zernike moments.^[1]

See also

• Zernike polynomials
• Image moment

References

1. Teh, C.-H.; Chin, R. (1988). "On image analysis by the methods of moments". IEEE Transactions on Pattern Analysis and Machine Intelligence 10 (4): 496–513. doi:10.1109/34.3913.

• Belkasim, S.; Ahmadi, M.; Shridhar, M. (1996). "Efficient algorithm for the fast computation of zernike moments". J. Franklin Institute 333 (4): 577–581. doi:10.1016/0016-0032(96)00017-8.
• Haddadnia, J.; Ahmadi, M.; Faez, K. (2003). "An efficient feature extraction method with pseudo-zernike moment in rbf neural network-based human face recognition system". EURASIP Journal on Applied Signal Processing 2003 (9): 890–901. doi:10.1155/S1110865703305128.
• T.-W. Lin; Y.-F. Chou (2003). A comparative study of zernike moments. Proceedings of the IEEE/WIC International Conference on Web Intelligence. pp. 516–519. ISBN 0-7695-1932-6.
• Chong, C.-W.; Raveendran, P.; Mukundan, R. (2003). "The scale invariants of pseudo-Zernike moments". Pattern Anal. Applic. 6 (3): 176–184. doi:10.1007/s10044-002-0183-5.
• Chong, Chee-Way; Mukundan, R.; Raveendran, P. (2003). "An Efficient Algorithm for Fast Computation of Pseudo-Zernike Moments" (PDF). Int. J. Pattern Recogn. Artif. Int. 17 (6): 1011–1023. doi:10.1142/S0218001403002769.
• Shutler, Jamie (1992). "Complex Zernike Moments".