New computational methods for full and subset Zernike moments

The computation of Zernike radial polynomials contributes most of the computation time in computing the Zernike moments due to the involvement of factorial terms. The common approaches used in fast computation of Zernike moments are Kintner's, Prata's, coefficient and q-recursive methods. In this paper, we propose faster methods to derive the full set of Zernike moments as well as a subset of Zernike moments. A hybrid algorithm that uses Prata's, simplified Kintner's and coefficient methods is used to derive the full set of Zernike moments. In the computation of a subset of Zernike moments, we propose using the combination of Prata's, simplified Kintner's, coefficient and q-recursive methods. Fast computation is achieved by using the recurrence relations between the Zernike radial polynomials of successive order without any involvement of factorial terms. In the first and second experiments, we show both the hybrid algorithms take lesser computation time than the existing methods in computing the full set of Zernike moments and a selected subset of Zernike moments which are not in successive sequence. Both hybrid algorithms have been applied in real world application in the classification of rice grains using full set and subset of Zernike moments. The classification performance using optimal subset of Zernike moments is better than using full set of Zernike moments.