搜索资源列表
jengfeng_v72
- 利用贝叶斯原理估计混合logit模型的参数，可以得到很精确的幅值、频率、相位估计，高斯白噪声的生成程序。- Bayesian parameter estimation principle mixed logit model, You can get a very accurate amplitude, frequency, phase estimation, Gaussian white noise generator.
Gussian
- 基于混合高斯模型的背景减除法，用于分离前景和背景-Background subtraction method based on Gaussian mixture model for separating the foreground and background
Moving-Object-Detection
- 混合高斯模型，目标检测，目标分割，区域标记-Gaussian mixture model, target detection, target segmentation, region
Foreground-detection-procedures
- 前景检测程序，MATLAB实现，有背景差分，帧差法，混合高斯模型，光流法。-Foreground detection procedures, MATLAB, background difference, frame difference method, the gaussian mixture model and optical flow method.
Improved-hybrid-Gauss
- 本文主要是针对传统混合高斯模型运动目标检测算法的不足做出了一些改进-This article is for the shortcomings of traditional Gaussian mixture model moving object detection algorithm made some improvements
EmGm
- 混合高斯聚类分析模型，自带demo，下载可直接运行，有可视化结果-Mixed Gauss cluster analysis model, comes with demo, download can be run directly, there are visual results
mixture_of_gaussians
- 这是一个混合高斯模型的MATLAB程序，能够实现运动目标检测阶段，仅供初学者参考。-This is a mixed Gaussian movement target detection MATLAB program, can achieve the initial detection of moving objects, only for beginners reference
dangaosi
- 单高斯模型是一种图像处理背景提取的处理方法，适用于背景单一不变的场合，其他如混合高斯模型等方法都是对单高斯模型的扩展，单以单高斯模型最为简便，而且采取参数迭代方式，不用每次都进行建模处理。-Single Gaussian model is an image processing method for processing background extraction, suitable for constant background single occasion, other methods s
VIBE-master
- Vibe算法实现目标跟踪，目标检测,比混合高斯模型速度更快更准确-Vibe algorithm target tracking, target detection, faster and more accurate than Gaussian mixture model speed
Mixed-Gauss-model
- 混合高斯模型，内附源码、paper、测试结果、说明等。-Gaussian mixture model
nybbc
- 利用matlab针对图像进行马氏距离计算 ,高斯白噪声的生成程序，利用贝叶斯原理估计混合logit模型的参数。- Using matlab to calculate the Mahalanobis distance for the image, Gaussian white noise generator, Bayesian parameter estimation principle mixed logit model.
em
- em算法介绍:EM算法有很多的应用，最广泛的就是GMM混合高斯模型、聚类、HMM等等(This is the EM algorithm using JAVA, easy to understand, easy to use and helpful for understanding the EM algorithm)
讲课涉及到的一些源代码
- 一些关于混合高斯模型的程序，包括em方法和gibbish采样(some code relate to gmm)
GMM_EM
- GMM算法是混合高斯模型，其求解过程需要不断迭代，本程序利用EM算法进行了仿真实现，可以加深对GMM的理解。(GMM algorithm is a hybrid Gauss model, and its solution process needs iteration. This program uses EM algorithm for simulation, which can deepen the understanding of GMM.)
mixture_of_gaussians
- 视频前景信息提取的常用算法，广泛应用于视频检测报警等领域(A common algorithm for video foreground information extraction)
vbemgmm
- 在混合高斯模型参数估计方法上有很多方法，例如最大似然函数的EM算法，但是该算法容易出现过拟合，故本文提出了一个变分EM的算法来对参数进行估计，可以避免EM算法中的不足。 下面的示例文件中说明了使用下面的示例文件说明了用法 examplevbem，VBEM M示例文件 faithful.txt数据集为例(The parameters of Gauss mixture model estimation method has a lot of methods, such as the maxim
EmGm
- 主要是混合高斯模型的参数估计方法，运用的是最大似然函数EM算法。文件中包含训练数据。(The parameter estimation method of the mixed Gauss model is mainly based on the maximum likelihood function EM algorithm. The file contains training data.)
GMM_EM
- 混合高斯模型的参数计算方法，采用EM迭代的方法求得(Parameter calculation method of mixed Gauss model)
404440
- 混合高斯概率密度模型,其参数估计可以通过期望最大化( EM) 迭代算法获得,()
peel-lerher
- 混合高斯模型和EM算法结合,当中用到了自己写的Kmeans聚类,附带测试样例,训练样例和main函数,()