Invariant Image Recognition by Zernike Moments

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. >

[1]  von F. Zernike.  Beugungstheorie des schneidenver-fahrens und seiner verbesserten form, der phasenkontrastmethode , 1934 .

[2]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[3]  D. Varberg,et al.  Calculus with Analytic Geometry , 1968 .

[4]  King-Sun Fu,et al.  Syntactic Pattern Recognition And Applications , 1968 .

[5]  Ralph Roskies,et al.  Fourier Descriptors for Plane Closed Curves , 1972, IEEE Transactions on Computers.

[6]  Robert B. McGhee,et al.  Aircraft Identification by Moment Invariants , 1977, IEEE Transactions on Computers.

[7]  King-Sun Fu,et al.  Shape Discrimination Using Fourier Descriptors , 1977, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  S. Maitra.  Moment invariants , 1979, Proceedings of the IEEE.

[9]  M. Teague.  Image analysis via the general theory of moments , 1980 .

[10]  Rama Chellappa,et al.  Stochastic models for closed boundary analysis: Representation and reconstruction , 1981, IEEE Trans. Inf. Theory.

[11]  H H Arsenault,et al.  Rotation-invariant digital pattern recognition using circular harmonic expansion. , 1982, Applied optics.

[12]  Demetri Psaltis,et al.  Image Normalization by Complex Moments , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Alireza Khotanzad,et al.  Rotation invariant pattern recognition using Zernike moments , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[14]  Anthony P. Reeves,et al.  Three-Dimensional Shape Analysis Using Moments and Fourier Descriptors , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Adam Krzyzak,et al.  Reconstruction of two dimensional patterns by Fourier descriptors , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[16]  Roland T. Chin,et al.  On Image Analysis by the Methods of Moments , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .