Image analysis by log-polar Exponent-Fourier moments

摘要

Moments, as a popular class of the global invariant image descriptors, have been widely used in image analysis, pattern recognition and computer vision applications. Exponent-Fourier moments (EFMs) are a new set of orthogonal moments based on exponential functions, which are suitable for image analysis and rotation invariant pattern recognition. However, EFMs lack natively the scaling-invariant property. In addition, they always suffer from high time complexity, numerical instability, and reconstruction error, especially for higher order of moments. In this paper, we introduce a class of scaling and rotation-invariant orthogonal moments, named Log-Polar Exponent-Fourier moments (LPEFMs), by extending the classical EFMs to the log-polar coordinates. Firstly, we redefined the EFMs' basis functions in log-polar domain instead of Cartesian/polar coordinate domain in order to obtain the scaling-invariant property. Then, we develop a new framework for computing the LPEFMs by using pseudo-polar Fourier transform and frequency domain interpolation, which result in better image representation capability, numerical stability, and computational speed. Compared with the classical EFMs, the proposed LPEFMs have four advantages, scaling invariance, speed, accuracy and stability. Theoretical analysis and simulation results are provided to validate the proposed image moment and to compare its performance with previous works. (C) 2019 Elsevier Ltd. All rights reserved.