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[Graph program] 统计 DL : 0: 计算图像直方图的统计特征，包括均值，方差，熵，三阶矩等。-image histogram calculated the statistical characteristics, including mean, variance, entropy, such as third-order moments.
Date : 2008-10-13 Size : 970byte User : 夏玉
[Other resource] higher-orderstatistics DL : 0: ICA经典文章,入门必看。受益，高阶矩统计牲在信号处理中的应用，详细介绍许多重要概念。-ICA classic article, the entry Watchable. Benefit Higher Moments statistical offerings in signal processing applications, detailed many important concepts.
Date : 2008-10-13 Size : 2.12mb User : 郭文
[Graph program] 统计 DL : 0: 计算图像直方图的统计特征，包括均值，方差，熵，三阶矩等。-image histogram calculated the statistical characteristics, including mean, variance, entropy, such as third-order moments.
Date : 2025-07-01 Size : 1kb User : 夏玉
[AI-NN-PR] higher-orderstatistics DL : 0: ICA经典文章,入门必看。受益，高阶矩统计牲在信号处理中的应用，详细介绍许多重要概念。-ICA classic article, the entry Watchable. Benefit Higher Moments statistical offerings in signal processing applications, detailed many important concepts.
Date : 2025-07-01 Size : 2.12mb User : 郭文
[Special Effects] textureanalyse DL : 0: 纹理分析源代码,统计图片的能量、熵、矩等信息，图像处理比较不错的代码-Texture analysis of source code, statistical picture of energy, entropy, moments and other information, image processing relatively good code
Date : 2025-07-01 Size : 1.93mb User : 阳化
[Internet-Network] source DL : 0: 使用INTEL矢量统计类库的程序,包括以下功能:  Raw and central moments up to 4th order  Kurtosis and Skewness  Variation Coefficient  Quantiles and Order Statistics  Minimum and Maximum  Variance-Covariance/Correlation matrix  Pooled/Group Variance-Covariance/Correlation Matrix and Mean  Partial Variance-Covariance/Correlation matrix  Robust Estimators for Variance-Covariance Matrix and Mean in presence of outliers-INTEL vector statistical library use procedures, including the following features:  Raw and central moments up to 4th order  Kurtosis and Skewness  Variation Coefficient  Quantiles and Order Statistics  Minimum and Maximum  Variance-Covariance/Correlation matrix  Pooled/Group Variance-Covariance/Correlation Matrix and Mean  Partial Variance-Covariance/Correlation matrix  Robust Estimators for Variance-Covariance Matrix and Mean in presence of outliers
Date : 2025-07-01 Size : 112kb User : mktresearch
[Delphi VCL] MCSM DL : 0: Modeling continuous-stochastic models of computer and parameter identification by statistical moments.
Date : 2025-07-01 Size : 6.27mb User : sirius
[matlab] MATLABfacemomentsp DL : 0: Face Recognition Based on Statistical Moments
Date : 2025-07-01 Size : 32kb User : killer
[AI-NN-PR] jiejulaiguji DL : 0: M2M4-该代码是经典的信噪比估计算法，用到了统计学中基于矩估计的思想，利用信号的2、4阶矩来估计接收信号的信噪比-M2M4-the code is the classic SNR estimation algorithm used in the statistical moment estimation based on the idea, the signal of 2,4-order moments to estimate the signal to noise ratio of the received signal
Date : 2025-07-01 Size : 1kb User : 闫姗姗
[matlab] PropCode2 DL : 0: PropCode2 is a MATLAB implementation of the algorithm described in Chap- ter 3 of The Theory of Scintillation with Applications in Remote Sensing by Charles L. Rino, John Wiley & Sons IEEE Press, 2010. The algorithm simulates electromagnetic (EM) wave propagation in a fully three-dimensional medium. Although PropCode2 is a direct extension of PropCode1, it is congured to ex- plore the statistical theory of scintillation. The statistical theory connes the structure congurations to realizations of statistically homogeneous processes, as described in book Chapter 3. Homogeneous processes admit position invari- ant moments and a spectral density function (SDF). Turbulence is characterized by a power-law SDF.
Date : 2025-07-01 Size : 728kb User : Marko
[Special Effects] Zernike DL : 0: Zernike矩是一种具有尺度、移位和旋转不变性的正交不变矩，本设计的目的就是利用Zernike不变矩设计一种图像检索系统，该系统能够充分验证Zerinike矩的不变性及其在图像检索中的优良性能。具体内容包括： (1) 图像特征提取、统计特征提取； (2) Zernike不变矩及其应用方法； (3) 基于Zernike不变矩的图像检索系统。 -Zernike moments is a scale, shift and rotation invariant orthogonal invariant moments, the purpose of this design is the use of Zernike invariant moments design an image retrieval system, the system can fully verify Zerinike moments invariance and itsexcellent performance in image retrieval. Specific content includes: (1) image feature extraction, statistical feature extraction (2) Zernike invariant moments and its application method (3) based on Zernike Moment Invariant image retrieval system
Date : 2025-07-01 Size : 358kb User : hanlianfu
[matlab] hosa DL : 0: 最新、最全“高阶谱分析工具箱”，包括全部教程和DEMO.-There is much more information in a stochastic non-Gaussian or deterministic signal than is conveyed by its autocorrelation and power spectrum. Higher-order spectra which are defined in terms of the higher-order moments or cumulants of a signal, contain this additional information. The Higher-Order Spectral Analysis (HOSA) Toolbox provides comprehensive higher-order spectral analysis capabilities for signal processing applications. The toolbox is an excellent resource for the advanced researcher and the practicing engineer, as well as the novice student who wants to learn about concepts and algorithms in statistical signal processing. The HOSA Toolbox is a collection of M-files that implement a variety of advanced signal processing algorithms for the estimation of cross- and auto-cumulants (including correlations), spectra and olyspectra,bispectrum, and bicoherence, and omputation of time-frequency distributions. Based on these, algorithms for parametric and non-parametric blin
Date : 2025-07-01 Size : 2.75mb User : Peng Lv
[Graph program] 统计 DL : 0: 计算图像直方图的统计特征，包括均值，方差，熵，三阶矩等。-image histogram calculated the statistical characteristics, including mean, variance, entropy, such as third-order moments.
Date : 2025-07-01 Size : 1kb User : 马坊镇
[Program doc] PSKmodulation DL : 0: 提出一种全新的子载波调制方式盲识别算法,该算法利用ＯＦＤＭ子载波组的统计特性,然后通过推导得到新的基于混合高阶矩的特征量,使得到新的特征量不受信噪比、载波频偏与相位偏移的影响。 -Proposed a new sub-carrier modulation count Blind Identification Method, the method using the statistical characteristics of the OFDM sub-carrier group, and By mixing deduced based on the new higher moments of the characteristic quantities such that Not amount to a new feature to noise ratio, carrier frequency offset and phase offset Affected....
Date : 2025-07-01 Size : 152kb User : ght
[matlab] facemomentsp DL : 0: face recognition based on statistical moments
Date : 2025-07-01 Size : 286kb User : Many
[Industry research] csit2305 DL : 0: This paper presents a novel approach for detecting vehicles for driver assistance. Assuming flat roads, vanishing point is first estimated using Hough transform space to reduce the computational complexity. Localization of vehicles is carried using horizontal projection on the horizontal gradient image below vanishing point. An uppermost and lowermost peak in the horizontal profile corresponds to search space of vehicles. Binarization of search space on the horizontal gradient image is done using Otsu algorithm. Verification of vehicles is carried through a series of rule based classifiers constructed using statistical moments, observing peaks in vertical profiling, vehicle texture, symmetry and shadow property. Experimentation was carried out on flat highway roads and detection rate of vehicles is nearly found to be 88.23 -This paper presents a novel approach for detecting vehicles for driver assistance. Assuming flat roads, vanishing point is first estimated using Hough transform space to reduce the computational complexity. Localization of vehicles is carried using horizontal projection on the horizontal gradient image below vanishing point. An uppermost and lowermost peak in the horizontal profile corresponds to search space of vehicles. Binarization of search space on the horizontal gradient image is done using Otsu algorithm. Verification of vehicles is carried through a series of rule based classifiers constructed using statistical moments, observing peaks in vertical profiling, vehicle texture, symmetry and shadow property. Experimentation was carried out on flat highway roads and detection rate of vehicles is nearly found to be 88.23
Date : 2025-07-01 Size : 184kb User : Chidanand
[Graph Recognize] Moments-Center DL : 0: 3.Shape Descriptors Centroids (Center of Mass) 3.2 Statistical moments: Useful for describing the shape of boundary segments (or other curves) Suitable for describing the shape of convex deficiencies The histogram of the function (segment curve) can also be used for calculating moments 2nd moment gives spread around mean (variance) 3rd moment gives symmetry around mean (skewness) -3.Shape Descriptors Centroids (Center of Mass) 3.2 Statistical moments: Useful for describing the shape of boundary segments (or other curves) Suitable for describing the shape of convex deficiencies The histogram of the function (segment curve) can also be used for calculating moments 2nd moment gives spread around mean (variance) 3rd moment gives symmetry around mean (skewness)
Date : 2025-07-01 Size : 2kb User : mohammed
[matlab] PropCode2 DL : 0: PropCode2 is a MATLAB implementation of the algorithm described in Chap-ter 3 of The Theory of Scintillation with Applications in Remote Sensing byCharles L. Rino, John Wiley & Sons IEEE Press, 2010. The algorithm simulates electromagnetic (EM) wave propagation in a fully three-dimensional medium.Although PropCode2 is a direct extension of PropCode1, it is congured to ex-plore the statistical theory of scintillation. The statistical theory connes the structure congurations to realizations of statistically homogeneous processes, as described in book Chapter 3. Homogeneous processes admit position invari- ant moments and a spectral density function (SDF). Turbulence is characterized by a power-law SDF.
Date : 2025-07-01 Size : 727kb User : Dongjun
[Graph Recognize] UCI的光学字符识别数据集 DL : 0: 其目标是将大量黑白矩形像素显示器中的每一个识别为英文字母中的26个大写字母之一。字符图像基于20种不同的字体，并且这20种字体中的每个字母随机失真以产生20,000个独特刺激的文件。每个刺激被转换成16个基本的数字属性（统计矩和边缘计数），然后将其缩放以适合从0到15的整数值范围。我们通常在前16000个项目上进行训练，然后使用结果模型预测剩余的4000个字母类别。请参阅上面引用的文章以获取更多详细信息。(The objective is to identify each of a large number of black-and-white rectangular pixel displays as one of the 26 capital letters in the English alphabet. The character images were based on 20 different fonts and each letter within these 20 fonts was randomly distorted to produce a file of 20,000 unique stimuli. Each stimulus was converted into 16 primitive numerical attributes (statistical moments and edge counts) which were then scaled to fit into a range of integer values from 0 through 15. We typically train on the first 16000 items and then use the resulting model to predict the letter category for the remaining 4000. See the article cited above for more details.)
Date : 2025-07-01 Size : 522kb User : 那拍拍