Description: 自适应核密度估计运动检测方法
提出一种自适应的核密度(kernel density estimation, KDE)估计运动检测算法. 算法首先提出一种自适应前景、背景阈值的双阈值选择方法, 用于像素分类. 该方法用双阈值能克服用单阈值分类存在的不足, 阈值的选择能自适应进行, 且能适应不同的场景. 在此基础上, 本文提出了基于概率的背景更新模型, 按照像素的概率来更新背景, 并利用帧间差分背景模型和KDE分类结果, 来解决背景更新中的死锁问题, 同时检测背景的突然变化. 实验证明了所提出方法的适应性和可靠性.-Adaptive kernel density estimation method of motion detection of an adaptive kernel density (kernel density estimation, KDE) estimated motion detection algorithm. Algorithm first proposed an adaptive prospects, the background of the dual-threshold threshold selection method for pixel classification. The method used to overcome the dual-threshold single classification threshold shortcomings, the choice of threshold can be adaptive, and able to adapt to different scenes. On this basis, this paper, based on the probability of the background update model, in accordance with the probability of pixels to update the background, and take advantage of interframe differential background model and the classification results of KDE, to resolve the deadlock in the background update, while detection of abrupt changes in the background. Experiments show the proposed method of adaptability and reliability. Platform: |
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Author:maolei |
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Description: 统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含：
1，Analysis of linear discriminant function
2，Feature extraction: Linear Discriminant Analysis
3，Probability distribution estimation and clustering
4，Support Vector and other Kernel Machines-
This section should give the reader a quick overview of the methods implemented in
STPRtool.
• Analysis of linear discriminant function: Perceptron algorithm and multiclass
modification. Kozinec’s algorithm. Fisher Linear Discriminant. A collection
of known algorithms solving the Generalized Anderson’s Task.
• Feature extraction: Linear Discriminant Analysis. Principal Component Analysis
(PCA). Kernel PCA. Greedy Kernel PCA. Generalized Discriminant Analysis.
• Probability distribution estimation and clustering: Gaussian Mixture
Models. Expectation-Maximization algorithm. Minimax probability estimation.
K-means clustering.
• Support Vector and other Kernel Machines: Sequential Minimal Optimizer
(SMO). Matlab Optimization toolbox based algorithms. Interface to the
SVMlight software. Decomposition approaches to train the Multi-class SVM classifiers.
Multi-class BSVM formulation trained by Kozinec’s algorithm, Mitchell-
Demyanov-Molozenov algorithm Platform: |
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Author:查日东 |
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Description: 视频背景非参数估计论文及matlab实现.matlab代码只实现了灰度图的背景估计，论文利介绍的彩色视频处理方法可以自己看看怎么做。-Background and Foreground Modeling Using
Nonparametric Kernel Density Estimation for
Visual Surveillance
AHMED ELGAMMAL, RAMANI DURAISWAMI, MEMBER, IEEE, DAVID HARWOOD, AND
LARRY S. DAVIS, FELLOW, IEEE
Invited Paper
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Size: 258048 |
Author:flora |
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Description: 视频跟踪检测 背景核密度估计建模，欢迎指正，-Detection of the background video track modeling kernel density estimation, please correct me, thank you Platform: |
Size: 1024 |
Author:陈偲 |
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Description: 代码用于估计关联维数。包括G-P算法(corrint.m)，高斯核关联算法(gka.m) 和Judd算法(judd.m)-Correlation dimension estimation code. Algorithms for estimating the correlation dimension using the grassberger-Proccacia approach (corrint.m), the Gaussian-Kernel algorithm (gka.m) and Judd s estimator (judd.m) are provided. Platform: |
Size: 18432 |
Author:彭跃华 |
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Description: kde全称是kernel density estimation.基于核函数的概率密度估计方法。是模式识别中常用的算法之一-KDE which is kernel density estimation is used to estimate probabilty function. It is mostly used in pattern recogntion Platform: |
Size: 2274304 |
Author:herman |
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Description: 一种核密度估计，或者称作带宽选择的方法，可以估计二维尺度参数，至于多维以上的估计方法尚在开发，多维情况下个人经验好的方法是多次实验取较好值-kernel density estimation, bandwidth selection, two-dimensional scale parameter can be estimated ,for the multi-dimensional approaches are still under development, multi-dimensional cases, personal experience is to do the experiment many times Platform: |
Size: 623616 |
Author:苗晨 |
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Description: MATLAB程序，
半参数线性回归模型的最小二乘核估计
半参数线性回归模型的最小二乘正交序列估计。-MATLAB program, semi-parametric linear regression model of least squares kernel estimation Semiparametric least squares linear regression model orthogonal sequence estimation. Platform: |
Size: 1024 |
Author:wyh |
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Description: 以为高斯和密度估计，使用高斯核的非参数密度估计方法，对样本进行概率密度估计，程序中给出了窗宽的估算公式。-That the Gaussian and density estimation, using Gaussian kernel non-parametric density estimation method, the sample probability density estimates, the program gives the formula for bandwidth estimation. Platform: |
Size: 1024 |
Author:william |
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Description: 利用正态分布和核密度估计计算分位数。包括正态分布分位数函数、核估计概率密度函数、核估计累计分布概率函数、核估计计算分位数函数。-Normal and kernel density estimation using sub-digit calculation. Including the normal quantile function, kernel estimate probability density function, cumulative distribution probability function kernel estimation, kernel estimate calculated quantile function. Platform: |
Size: 6601728 |
Author:细细 |
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Description: 对数字图像去模糊，分两段来对运动模糊核函数进行估计，附图像处理的结果-As to digital images deblur, we use two-phase kernel estimation,including the results Platform: |
Size: 7152640 |
Author:Amanda |
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Description: 核密度估计 R语言编写的源代码 直接下载 帮助文件-Kernel density estimation written in R source code to download the help file Platform: |
Size: 4096 |
Author:qiaojian |
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Description: 文章介绍了两种方法来解释误差，Inverse Halftoning and Kernel Estimation
for Error Diffusion。比较了这两种方法的优劣-Article describes two ways to explain the error, Inverse Halftoning and Kernel Estimation for Error Diffusion. Compared the advantages and disadvantages of both methods Platform: |
Size: 1425408 |
Author:xuzhitong05 |
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