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Moment functions deÿned using a polar coordinate representation of the image space, such as radial moments and Zernike moments, are used in several recognition tasks requiring rotation invariance. However, this coordinate representation does not easily yield translation invariant functions, which are also widely sought after in pattern recognition(More)
By convention, the translation and scale invariant functions of Legendre moments are achieved by using a combination of the corresponding invariants of geometric moments. They can also be accomplished by normalizing the translated and/or scaled images using complex or geometric moments. However, the derivation of these functions is not based on Legendre(More)
Three-dimensional digital images are gaining more attention in pattern recognition field. Mostly literatures, however, only focus on theoretical framework of two-dimensional moment invariants, that are only implemented on two-dimensional images. Consequently, it reduces the invariance flexibility to support three-dimensional objects. In this paper, we(More)
Pseudo-Zernike moments have better feature representation capabilities and are more robust to image noise than the conventional Zernike moments. However, pseudo-Zernike moments have not been extensively used as feature descriptors due to the computational complexity of the pseudo-Zernike radial polynomials. This paper discusses the drawbacks of the existing(More)
This paper details a comparative analysis on time taken by the present and proposed methods to compute the Zernike moments, Zpq. The present method comprises of Direct, Belkasim's, Prata's, Kintner's and Coeecient methods. We propose a new technique, denoted as q-recursive method, speciÿcally for fast computation of Zernike moments. It uses radial(More)
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