- Source: 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
n
m
(
x
,
y
)
=
R
n
m
(
x
,
y
)
e
j
m
arctan
(
y
x
)
,
{\displaystyle V_{nm}(x,y)=R_{nm}(x,y)e^{jm\arctan({\frac {y}{x}})},}
where
x
2
+
y
2
≤
1
,
n
≥
0
,
|
m
|
≤
n
{\displaystyle x^{2}+y^{2}\leq 1,n\geq 0,|m|\leq n}
and orthogonality on the unit disk is given as
∫
0
2
π
∫
0
1
r
[
V
n
l
(
r
cos
θ
,
r
sin
θ
)
]
∗
×
V
m
k
(
r
cos
θ
,
r
sin
θ
)
d
r
d
θ
=
π
n
+
1
δ
m
n
δ
k
l
,
{\displaystyle \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
{\displaystyle r^{2}=x^{2}+y^{2}}
,
x
=
r
cos
θ
{\displaystyle x=r\cos \theta }
,
y
=
r
sin
θ
{\displaystyle y=r\sin \theta }
are the standard transformations between polar and Cartesian coordinates.
The radial polynomials
R
n
m
{\displaystyle R_{nm}}
are defined as
R
n
m
(
r
)
=
∑
s
=
0
n
−
|
m
|
D
n
,
|
m
|
,
s
r
n
−
s
{\displaystyle R_{nm}(r)=\sum _{s=0}^{n-|m|}D_{n,|m|,s}\ r^{n-s}}
with integer coefficients
D
n
,
|
m
|
,
s
=
(
−
1
)
s
(
2
n
+
1
−
s
)
!
s
!
(
n
−
|
m
|
−
s
)
!
(
n
+
|
m
|
−
s
+
1
)
!
.
{\displaystyle 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
{\displaystyle R_{0,0}=1}
R
1
,
0
=
−
2
+
3
r
{\displaystyle R_{1,0}=-2+3r}
R
1
,
1
=
r
{\displaystyle R_{1,1}=r}
R
2
,
0
=
3
+
10
r
2
−
12
r
{\displaystyle R_{2,0}=3+10r^{2}-12r}
R
2
,
1
=
5
r
2
−
4
r
{\displaystyle R_{2,1}=5r^{2}-4r}
R
2
,
2
=
r
2
{\displaystyle R_{2,2}=r^{2}}
R
3
,
0
=
−
4
+
35
r
3
−
60
r
2
+
30
r
{\displaystyle R_{3,0}=-4+35r^{3}-60r^{2}+30r}
R
3
,
1
=
21
r
3
−
30
r
2
+
10
r
{\displaystyle R_{3,1}=21r^{3}-30r^{2}+10r}
R
3
,
2
=
7
r
3
−
6
r
2
{\displaystyle R_{3,2}=7r^{3}-6r^{2}}
R
3
,
3
=
r
3
{\displaystyle R_{3,3}=r^{3}}
R
4
,
0
=
5
+
126
r
4
−
280
r
3
+
210
r
2
−
60
r
{\displaystyle R_{4,0}=5+126r^{4}-280r^{3}+210r^{2}-60r}
R
4
,
1
=
84
r
4
−
168
r
3
+
105
r
2
−
20
r
{\displaystyle R_{4,1}=84r^{4}-168r^{3}+105r^{2}-20r}
R
4
,
2
=
36
r
4
−
56
r
3
+
21
r
2
{\displaystyle R_{4,2}=36r^{4}-56r^{3}+21r^{2}}
R
4
,
3
=
9
r
4
−
8
r
3
{\displaystyle R_{4,3}=9r^{4}-8r^{3}}
R
4
,
4
=
r
4
{\displaystyle R_{4,4}=r^{4}}
R
5
,
0
=
−
6
+
462
r
5
−
1260
r
4
+
1260
r
3
−
560
r
2
+
105
r
{\displaystyle R_{5,0}=-6+462r^{5}-1260r^{4}+1260r^{3}-560r^{2}+105r}
R
5
,
1
=
330
r
5
−
840
r
4
+
756
r
3
−
280
r
2
+
35
r
{\displaystyle R_{5,1}=330r^{5}-840r^{4}+756r^{3}-280r^{2}+35r}
R
5
,
2
=
165
r
5
−
360
r
4
+
252
r
3
−
56
r
2
{\displaystyle R_{5,2}=165r^{5}-360r^{4}+252r^{3}-56r^{2}}
R
5
,
3
=
55
r
5
−
90
r
4
+
36
r
3
{\displaystyle R_{5,3}=55r^{5}-90r^{4}+36r^{3}}
R
5
,
4
=
11
r
5
−
10
r
4
{\displaystyle R_{5,4}=11r^{5}-10r^{4}}
R
5
,
5
=
r
5
{\displaystyle R_{5,5}=r^{5}}
Moments
The pseudo-Zernike Moments (PZM) of order
n
{\displaystyle n}
and repetition
l
{\displaystyle l}
are defined as
A
n
l
=
n
+
1
π
∫
0
2
π
∫
0
1
[
V
n
l
(
r
cos
θ
,
r
sin
θ
)
]
∗
f
(
r
cos
θ
,
r
sin
θ
)
r
d
r
d
θ
,
{\displaystyle 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
,
…
∞
{\displaystyle n=0,\ldots \infty }
, and
l
{\displaystyle l}
takes on positive and negative integer
values subject to
|
l
|
≤
n
{\displaystyle |l|\leq n}
.
The image function can be reconstructed by expansion of the pseudo-Zernike coefficients on the unit disk as
f
(
x
,
y
)
=
∑
n
=
0
∞
∑
l
=
−
n
+
n
A
n
l
V
n
l
(
x
,
y
)
.
{\displaystyle 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.
See also
Zernike polynomials
Image moment
References
Belkasim, S.; Ahmadi, M.; Shridhar, M. (1996). "Efficient algorithm for the fast computation of zernike moments". Journal of the 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. Bibcode:2003EJASP2003..146H. 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. doi:10.1109/WI.2003.1241255. ISBN 0-7695-1932-6.
Chong, C.-W.; Raveendran, P.; Mukundan, R. (2003). "The scale invariants of pseudo-Zernike moments" (PDF). 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. hdl:10092/448.
Shutler, Jamie (1992). "Complex Zernike Moments".
