Image analysis using separable two-dimensional discrete orthogonal moments
Hongqing Zhu · 2011
This paper presents three new separable 2-D discrete orthogonal moments. The kernel functions of the proposed Meixner Krawtchouk moments (MKM), Tchebichef-Charlier moments (TCM), and Meixner-Hahn moments (MHM) are mutually orthogonal and separable. Unlike the traditional 2-D discrete orthogonal moments, in the proposed separable 2-D discrete orthogonal moments, the kernel functions can be expressed as two separable terms by producing two different classical orthogonal polynomials of a variable. Specifically, the tense product of Meixner and Krawtchouk polynomials can be used to generate kernel functions for 2-D discrete orthogonal MKM. The global extraction capabilities of proposed moments are described by analyzing the reconstructed image's accuracy. The experimental results show that these proposed moments have better image description capabilities.