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Invariant Image Recognition by Zernike Moments
May 1990 (vol. 12 no. 5)
pp. 489-497

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.

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Index Terms:
scale invariance; invariant image recognition; Zernike moments; rotation-invariant features; translation invariance; geometrical moments; orthogonality; image reconstruction; feature selection; pattern recognition; picture processing
Citation:
A. Khotanzad, Y.H. Hong, "Invariant Image Recognition by Zernike Moments," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 5, pp. 489-497, May 1990, doi:10.1109/34.55109
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