有效地形匹配的三维正交矩谱分析

IF 7.6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Pub Date : 2025-07-26 DOI:10.1016/j.patcog.2025.112212

Junjie Zhou , Quan Hu , Kedong Wang , Jinling Wang

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引用次数: 0

摘要

为了提高地形匹配性能，首次对典型的三维正交矩进行了光谱分析。典型的三维矩阵包括Zernike矩（ZM）、正交Fourier-Mellin矩（OFMM）、分数阶Jacobi-Fourier矩（FJFM）、指数- fourier矩（EFM）、一般极复指数变换（GPCET）和贝塞尔- fourier矩（BFM）。首先推导了典型三维OMs在空间域和频域的一般表达式。然后，采用傅里叶中心切片法对地形谱进行分析。进一步研究了典型三维OMs的球谐谱和径向谱。在对典型三维OMs进行光谱分析的基础上，设计了相应的地形匹配算法。数值实验表明，算法的匹配性能与典型三维OMs的光谱分析结果吻合。这些新发现为设计有效的地形匹配算法奠定了基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Spectral analysis on 3D orthogonal moments for effective terrain matching

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Spectral analysis on 3D orthogonal moments for effective terrain matching

Aiming to improve the terrain matching performance, the spectral analysis on the typical 3D orthogonal moments (OMs) is accomplished for the first time. The typical 3D OMs include Zernike moments (ZM), orthogonal Fourier-Mellin moments (OFMM), fractional-order Jacobi-Fourier moments (FJFM), exponent-Fourier moments (EFM), generic polar complex exponent transform (GPCET), and Bessel-Fourier moments (BFM). The general expression of the typical 3D OMs in both the spatial and the frequency domains is derived firstly. Then, the terrain spectrum is analyzed by the Fourier central slicing method. Both the spherical harmonic and the radial spectra of the typical 3D OMs are investigated further. Based on the spectral analysis on the typical 3D OMs, the terrain matching algorithms are designed accordingly. Numerical experiments indicate that the matching performance of the algorithms coincides with the spectral analysis results of the typical 3D OMs. These new findings are the foundation for designing an effective terrain matching algorithm.

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来源期刊

Pattern Recognition 工程技术-工程：电子与电气

CiteScore

14.40

自引率

16.20%

发文量

683

审稿时长

5.6 months

期刊介绍： The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.