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- extracts foreground from background()
GMM_Background_subtraction-master
- 前景检测 多角度运动背景识别检测 静态监测(Foreground detection)
ML
- GMM高斯混合模型EM算法聚类，PCA主成分分析，以及从人脸图像中提取主成分(GMM Gauss hybrid model EM algorithm clustering, PCA principal component analysis, and extraction of principal components from face images)
multiobjecttracking - finally
- 本文件可实现多目标的跟踪，下载之后，将代码37行的视频名字改成自己名字，视频制作成压缩包中范本即可。如果对自己的视频文件效果不是很好，可改变GMM的参数，提高效果。(multiobject tracking)
spkID
- 利用mFCC特征提取算法进行语音信号的特征提取，然后利用ＧＭＭ识别出特征人，计算目标得分，程序效果ＯＫ。(The extraction algorithm for feature extraction of speech signal using mFCC features, and then use GMM to identify a specific target, calculate the score, the effect of OK program.)
OpenCVGMMComment
- OpenCV库中对于混合高斯模型我自己的注释，通俗易懂，并且与论文相结合，附上了例程(OpenCV GMM comment by zyx)
文献翻译
- 这是一篇关于Grabcut方法提取前景图像的文章，内有关于此方面的详细讲解。(This is an article about the extraction of foreground images by the Grabcut method, and there is a detailed explanation of this.)
object dection
- 给出一段有关人行为的视频，视频数据来源于KTH数据库，程序可以提取到我们所关心的前景区域。(GMM,C++, object dection)
GMM
- 一种较好的背景减除算法，可以提取运动物体的前景图像，并能较好进行运用(Background subtraction algorithm)
ClusterDataUsingAGaussianMixtureModelExample
- Cluster Data Using A Gaussian Mixture Model Example
Voice Similarity
- 基于GMM的声纹识别算法与人听觉的比较，目前关于声音相似度的研究比较少 ，这篇会有一点帮助(Voice Similarity - a Comparison Between Judgements by Human Listeners and Automatic Voice Comparison)
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)
sml
- 使用某类图像作为训练样本，基于SML算法的类模型，(Class model based on SML algorithm)
SML
- 基于DCT-GMM的SML的图片聚类功能的算法实现，使用MATLAB语言(DCT-GMM based SML image clustering algorithm implementation, using the MATLAB language)
EM-GMM.py
- Gaussian mixture 的 EM算法(EM algorithm for Gaussian mixture model)
gmm(2)
- 基于码本（codebook）的背景建模的背景差分法+级联基于LBK或haar的adaboost和基于hog的svm分类器+快速hough圆变换进行人头识别(Background modeling based on codebook (codebook) background difference method + cascade based on LBK or Haar AdaBoost and hog based SVM Classifier + fast Hough circle trans
数据
- gmm运行的调试命令stata软件操作方法(gmm code the use of stata,command of stata)
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.)
mfcc
- 通过语音识别对类型进行分类，使用HMM-GMM模型(Classification is done by speech recognition, using HMM-GMM model.)
EM 算法
- 用EM算法求解高斯混合模型并可视化，数据是男女生的身高分布，前提是初始化男女生身高各自的均值和方差和比例，然后由EM算法求解，男女生身高的均值方差，以拟合数据。(The EM algorithm is used to solve the Gauss mixture model and visualize. The data is the height distribution of male and female. The premise is to initialize the mean, v