Invariant image recognition by Zernike moments

Summary

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, makes the suggested feature selection approach practical. Features of each order can also be weighted according to their contribution to the reconstruction process. The method was tested using clean and noisy images from a 26-class character data set and a 4-class lake data set, and the superiority of Zernike moment features over regular moments and moment invariants was experimentally verified.

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