Description: 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 Platform: |
Size: 114688 |
Author:mktresearch |
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Description: 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 Platform: |
Size: 1024 |
Author:闫姗姗 |
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Description: 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 con gured to ex-
plore the statistical theory of scintillation. The statistical theory con nes the
structure con gurations 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. Platform: |
Size: 745472 |
Author:Marko |
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Description: 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 Platform: |
Size: 366592 |
Author:hanlianfu |
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Description: 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 Platform: |
Size: 2880512 |
Author:Peng Lv |
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Description: 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.... Platform: |
Size: 155648 |
Author:ght |
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Description: 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 Platform: |
Size: 188416 |
Author:Chidanand |
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Description: 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)
Platform: |
Size: 2048 |
Author:mohammed |
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Description: 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 con gured to ex-plore the statistical theory of scintillation. The statistical theory con nes the structure con gurations 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. Platform: |
Size: 744448 |
Author:Dongjun |
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Description: 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.) Platform: |
Size: 534528 |
Author:那拍拍 |
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Search - statistical moments