Generic orthogonal moments: Jacobi-Fourier moments for invariant image description

被引：83

作者：

Ping, Ziliang ^{[1
]}

Ren, Haiping

Zou, Jian

Sheng, Yunlong

Bo, Wurigen

机构：

[1] Inner Mongolia Normal Univ, Hohhot 010022, Peoples R China

[2] Natl Inst Control Pharmaceut & Biol Prod, Beijing 100050, Peoples R China

[3] Univ Laval, Dept Phys, Ste Foy, PQ G1K 7P4, Canada

[4] Beijing Univ, Math Inst, Beijing 100871, Peoples R China

来源：

PATTERN RECOGNITION | 2007年 / 40卷 / 04期

基金：

北京市自然科学基金;

关键词：

Jacobi polynomial; multi-distorted invariance; Jacobi-Fourier Moments; image reconstruction error; noise sensibility;

D O I：

10.1016/j.patcog.2006.07.016

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A multi-distorted invariant orthogonal moments, Jacobi-Fourier Moments (JFM), were proposed. The integral kernel of the moments was composed of radial Jacobi polynomial and angular Fourier complex componential factor. The variation of two parameters in Jacobi polynomial alpha and beta can form various types of orthogonal moments: Legendre-Fourier Moments (alpha = 1, beta = 1); Chebyshev-Fourier Moments (alpha =2, beta =(3/)(2) ); Orthogonal Fourier-Mellin Moments (alpha = 2, beta = 2); Zernike Moments and pseudo-Zernike Moments, and so on. Therefore, Jacobi-Fourier Moments are generic expressions of orthogonal moments formed by a radial orthogonal polynomial and angular Fourier complex component factor, providing a common mathematical too] for performance analysis of the orthogonal moments. In the paper, Jacobi-Fourier Moments were calculated for a deterministic image, and the original image was reconstructed with the moments. The relationship between Jacobi-Fourier Moments and other orthogonal moments was studied. Theoretical analysis and experimental investigation were conducted in terms of the description performance and noise sensibility of the JFM. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

引用

页码：1245 / 1254

页数：10

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