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详细介绍二维经验模式分解的一个PPT，尤其是在Huang教授的研讨会上的一个PPT，值得一看,Detailed two-dimensional empirical mode decomposition of a PPT, particularly in the seminar, Professor Huang, a PPT, worth a visit
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二维经验模式分解，速度较快，不错的代码，希望对学习HHT的朋友有用-Two-dimensional empirical mode decomposition, fast, good code
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BEMD对图像进行分解
Detailed two-dimensional empirical mode decomposition -BEMD decomposition of the image
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一个比较经典的二维经验模式分解程序代码，适用于图像的特征提取和分解-A more classic two-dimensional empirical mode decomposition procedure code, applied to image feature extraction and decomposition
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二维经验模式分解代码，十分实用，希望对有需要的朋友有用-Two-dimensional empirical mode decomposition code, are very useful, want to be useful to have a friend in need
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个比较经典的二维经验模式分解程序代码，适用于图像分解，加油-Comparison the classical two-dimensional empirical mode decomposition code for the image decomposition, refueling
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基于快速二维经验模态分解的纹理分割 -Based on rapid two-dimensional empirical mode decomposition of the texture segmentation
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Use the empirical mode decomposition method to analyze one dimensional signals.
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二维经验模态分解，采用形态学求极值点，径向基神经网络拟合，速度较快。-Two-dimensional empirical mode decomposition, using morphology for extreme points, radial basis function neural network fitting, faster.
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基于经验模式分解的完整代码代码，可以对加了噪声的信号进行多层分解，求取imf值，并并进行hht变换，得到相应的一维、二维、三维谱图-Empirical mode decomposition code, can be to multi-layer decomposition of signal with noise, obtain the fund value, and the HHT transform, and get the corresponding one-dimensional, tw
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二维经验模态分解的一个程序，改进了插值算法。输入为图像灰度图-A program of two-dimensional empirical mode decomposition and improved interpolation algorithm. Enter the grayscale image
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使用二维经验模态分解进行图像的去噪操作，内含三幅测试图片和说明文档-Using Bi-dimensional Empirical Mode Decomposition for image denoising operation
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利用matlab进行木材图像去噪的基本算法包含二维经验模态分解和一些经典去噪程序-The basic algorithm matlab to use timber that contains a two-dimensional image denoising empirical mode decomposition and some classical denoising program
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二维经验模态分解(BEMD)是近年来新兴的一种适用于非线性、非平稳数据的自适应数据(Two-dimensional empirical mode decomposition (BEMD) is a newly developed adaptive data for non-linear and non-stationary data in recent years.)
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