Some Aspects Of Fractional-Order Circular Moments For Image Analysis

In this paper, we briefly review the fractional-order circular moments, such as fractional-order Zernike moments, fractional-order Fourier-Mellin moments, fractional-order Legendre-Fourier moments, and fractional-order Chebyshev-Fourier moments, which can characterize, analyze, and manipulate the information contained in an image with minimal redundancy. Also, they depend on an a parameter for better feature extraction. Therefore, we propose alpha procedure to find the optimal alpha in terms of image reconstruction error and classification. We validate the search for the best rotation-invariant features using the MNIST and MNIST-R datasets. Finally, we present the study results and conclusions. (C) 2021 Elsevier B.V. All rights reserved.