搜索资源列表
Wavelet-feature-extraction
- 输入一幅图像的像素数据,可以提取该图像的小波特征数据,该数据存储后可以供以后的图像处理操作。-The pixel data of the input image can be extracted the wavelet characteristic data of the image, after the data storage can be used for subsequent image processing operations.
haartest
- 这是关于小波特征变换,其中这里的提取特征分为几个一是零中心归一化瞬时幅度的频谱最大值,二是包络平方值的两倍包络均值平方之差。三是瞬时频率的方差。四是大小波系数的方差,五是信号的四阶累积量之比。-This is the wavelet transform features, which extract features here is divided into several one is zero center normalized spectral maximum of instantaneo
generate_Gabor_features
- 基于matlab的人脸表情是别的Gabor小波特征提取-Matlab-based face expression is other Gabor little potter character extraction
mome_kmean_wavelet4
- 其中包括颜色矩特征提取,四层小波特征提取以及kmeans聚类算法,Matlab编程实现,希望对学习有帮助-Including the extraction of color moment feature, the four layer wavelet feature extraction and kmeans clustering algorithm, Matlab programming, and they hope to help with learning
GABOR
- 使用matlab里的函数来提取Gabor小波特征-Matlab function to use in feature extraction Gabor wavelet
GABOR-(2)
- 使用matlab里的函数来提取Gabor小波特征-Matlab function to use in feature extraction Gabor wavelet
haar
- 关于小波变换学习的一些源代码,提取小波特征-some of the source code of wavelet transform ,extract haar wavelet feature
wavelt-and-GrayGradinet
- 小波特征提取和灰度共生矩阵对图线特征进行提取-Wavelet feature extraction and GLCM feature extraction of plot
Force_TimeDomain
- 对动态力信号的时域特征进行提取包括谐波特征,能量特征和小波特征等-Characterized in the time domain dynamic force signal feature extraction, including harmonics, energy and wavelet characteristics
wavelet
- 用于提取小波特征的机械振动信号,很好地结合了特征提取和随机信号处理方法,附带数据,实测可用。-For extracting wavelet feature mechanical vibration signal, a good combination of feature extraction and stochastic signal processing method accompanying data, we found available.
db10
- 小波基为db的小波变换,可以提取输入图像的小波特征,低频高频的信息,然后得到特征后用于分类或识别-Wavelet transform is a wavelet transform, which can extract the wavelet features of the input image, low frequency and high frequency information, and then get the feature for classification or recogni
PCA_gabor_svm
- Gabor小波变换和PCA降维在用SVM分类(Gabor wavelet transform and PCA dimension reduction are classified in SVM)
eeg stroke
- 一些脑电数据和特征提取,提取的是小波包能量(Some EEG data and feature extraction are extracted from wavelet packet energy)
wavelet
- 一种时间尺度分析方法,在时率两域都具有表征信号局部特征的能力,很适合于探测正常信号中夹带的瞬间反常现象,很适合分析气候变化等问题。此例中,用于揭示某区域的气候变化趋势(A time scale analysis method has the capability of representing local characteristics of signals in time domain and two domain. It is very suitable for detecting the
Gabor
- 二维gabor变换提取图像的空间多尺度多方向的特征(2 dimensional gabor transform)
Gabor Wavelet Feature Extraction
- Gabor小波进行特征提取,实现人脸识别(Gabor Wavelet Feature Extraction to Achieve Face Recognition)
Gabor
- 用GABOR进行特征提取,通过gabor这种小波变换进行图像的roi区域的特征提取。(feature extraction of gabor)
程序
- main_feature.m:为特征提取主要程序,其中调用filter50.m子程序为50HZ工频滤噪;调用ApEn.m c0complex.m kEn_correct.m lyapunov_wolf.m LZC.m spectral_entropy.m SVDen.m SampEn.m子程序为非线性特征(近似熵,C0复杂度,K熵等)提取;wave_brain为小波分析频段特征提取。其中采样频率皆为256HZ。(ApEn.m c0complex.m kEn_correct.m lyapunov_
Desktop
- 西储大学轴承数据,故障特征频率明显,与理论值基本符合。(The bearing data of Xichuan University have obvious fault characteristic frequency, which is basically in accordance with the theoretical value.)
基于 HHT 的船体结构应力监测数据 特征分析和去噪方法
- [目的]为了去除船体结构应力监测数据中的噪声信号,获得有效的数据信息,以便为后续数据挖掘提 供支撑,[方法]首先,采用 HHT 方法中的经验模态分解(EMD)算法对数据进行成分分析,得到固有模态函数 (IMF)和余项。然后,通过 Hilbert变换得到 Hilbert谱,证明应力监测数据的非平稳特性。最后,以信噪比(SNR) 和均方根误差(RMSE)为例,结合自适应去噪和小波阈值去噪两种方法对应力监测数据进行去噪效果比较。 [结果]结果表明,基于 HHT方法的自适应去噪和小波去噪都具有一定