Fast Computation of Hahn Polynomials for High Order Moments

TL;DR

This paper introduces an efficient and stable computational method for high-order discrete Hahn polynomials, improving numerical stability and computational efficiency in applications like image processing.

Contribution

A new mathematical model and recurrence algorithms are developed to compute Hahn polynomials reliably at high orders, outperforming existing methods.

Findings

Better stability for high-order moments

Reduced computational cost

Extended maximum polynomial size

Abstract

Discrete Hahn polynomials (DHPs) and their moments are considered to be one of the efficient orthogonal moments and they are applied in various scientific areas such as image processing and feature extraction. Commonly, DHPs are used as object representation; however, they suffer from the problem of numerical instability when the moment order becomes large. In this paper, an efficient method for computation of Hahn orthogonal basis is proposed and applied to high orders. This paper developed a new mathematical model for computing the initial value of the DHP and for different values of DHP parameters ($\alpha$ and $\beta$). In addition, the proposed method is composed of two recurrence algorithms with an adaptive threshold to stabilize the generation of the DHP coefficients. It is compared with state-of-the-art algorithms in terms of computational cost and the maximum size that can be correctly generated. The experimental results show that the proposed algorithm performs better in both parameters for wide ranges of parameter values of ($\alpha$ and $\beta$) and polynomial sizes.