一种新的二维经验模态分解曲面插值方法

Cuiyun Li, Pan Han, H. Ji
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引用次数: 3

摘要

散射点的曲面插值是二维经验模态分解(BEMD)算法中的关键技术,它决定了二维内禀模态函数(BIMF)的质量。传统的插值方法通常具有较高的复杂度和边界效应。提出了一种基于Nadaray-Watson滤波的非参数核回归(KR)插值方法。滤波参数可对插值精度进行修改,滤波方向自适应,保持结果曲面的特征。仿真结果表明,基于KR的曲面插值可以降低算法的复杂度,避免边界效应,提高BEMD在边缘检测中的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Surface Interpolation Approach for Bidimensional Empirical Mode Decomposition
The surface interpolation of the scattered points is the key technology in Bidimensional Empirical Mode Decomposition (BEMD) algorithm that determines the quality of Bidimensional Intrinsic Mode Functions (BIMF). The traditional interpolation method commonly has high complexity and often results in boundary effects. A new interpolation method based on the nonparametric kernel regression (KR) with the Nadaray-Watson filter is proposed. The accuracy of the interpolation can be modified by the filter parameter, and the direction of the filter is adaptive to maintain the characteristic of the result surface. Simulation results show that the surface interpolation based on KR can reduce the complexity of the algorithm and avoid the boundary effects, improving the performance of BEMD in edge detection.
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