搜索资源列表
Classification
- 分类器程序，混合高斯分类器，用于语音图像的分类处理-classifier procedures, Gaussian mixture classifier for the classification of voice processing images
gaosi
- 图形图像处理，混合高斯图像背景估计＆更新-Graphic image processing, mixed-Gaussian background image is estimated
EMGMMSeg
- 对图像进行GMM（混合高斯）拟合后用EM算法进行分割-Image GMM (Gaussian Mixture) after fitting algorithm using EM Segmentation
EMGMM
- 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。-Gaussian Mixture Model and EM algorithms, which use their own written Kmeans cluster, with the test sample, the training sample and the main function.
imputation.tar
- 一个很有用的EM算法程序包，可用于混合高斯模型，值得一看哦-A useful package of the EM algorithm can be used in mixed-Gaussian model, see Oh
20081004
- GMM 混合高斯自适应背景维护 可以用来做行人检测 等-GMM Gaussian Mixture adaptive background maintenance can be used to make the pedestrian detection
2119413023220076181125249677325rar
- 可用于视频目标检测中背景生成的混合高斯模型-Can be used for video object detection in the context of generating a mixture of Gaussian model
混合高斯
- 用于车辆检测背景建模 通过混合高斯将前景与北京分离(Vehicle tracking background modeling is used to extract foreground)
EM_GM_fast
- 高斯混合模型中的EM算法（就不完整数据的极大似然估计）应用(EM algorithm in Gauss mixture model)
GMM
- 此算法实现高斯混合，可以对初始聚类算法选择c均值和EM，可以实现密度估计和分类。(This GMM algorithm can estimate the density and class, the initial steps can select the C-mean and EM.)
GMM
- 高斯混合聚类的python实现代码，里面有data的demo(Python implementation code of Gauss mixed clustering)
gmm
- 基于高斯混合模型的运动目标检测，opencv平台，直接可用(Moving target detection of Gauss mixed model)
GMMs
- function对数据EM算法进行fit，并对产生的高斯混合模型的最大似然估计进行绘图。输出结构体obj，带有高斯混合模型的参数mu，sigma。(Function carries out fit for data EM algorithm, and draws the maximum likelihood estimation of the Gauss mixture model. The output structure is ob
GMM
- 实现了EM算法对高斯混合模型进行聚类，并将聚类结果用图像展示出来，希望对混合模型的朋友有用。(The EM algorithm is implemented to cluster the Gauss mixture model, and the clustering results are displayed with images, hoping to be useful to friends of the mixed models.
RCY-GMMtest1
- 高斯混合模型（GMM,Gaussian Mixture Model）参数如何确立这个问题，详细讲解期望最大化（EM,Expectation Maximization）算法的实施过程。(How to establish the parameters of Gauss mixture model and explain the implementation process of the expectation maximization al
GMM_test1
- 高斯混合模型的前景提取代码，本人测试可用。(Gauss mixture model of the foreground extraction code)
Experient4
- 利用opencv高斯混合背景建模，并进行开闭运算滤波， 提取视频监控中的车辆(Using opencv Gaussian mixture background modeling and opening and closing operation filtering to extract vehicles in video surveillance)
3-基于高斯混合模型的语音识别
- 基于高斯混合模型的语音识别，有完整的数据集和matlab代码(Speech recognition based on Gaussian mixture model, complete data set and matlab code)
GMM-HMRF
- 基于高斯混合模型和隐马尔科夫模型的图像分割算法(Image segmentation algorithm based on Gaussian mixture model and hidden Markov model)
BIC确定GMM聚类簇数
- 通过贝叶斯信息准则确定高斯混合聚类方法的聚类簇数(Determining the Cluster Number of GMM Clusters by BIC)