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OneCutWithSeeds_v1.03
- one cut 算法是微软研究院发表的一篇关于前背景图像分割论文，用户只需简单地交互，即可快速分割前背景，代码比较清晰，内部graph cut和gmm等一些算法可以拿来学习或借鉴。-one cut algorithm is the Microsoft Research published a paper on the front background image segmentation, the user simply interact, you can quickly split befor
MixtGaussian
- 在matlab环境运行。基于GMM的说话人识别程序源代码，可直接运行。有详细的文件资料-Matlab environment runs. GMM based speaker recognition program source code, can be directly run. Detailed documentation
W5
- 视频帧序列实现基于GMM（高斯混合模型）的背景建模-Video frame sequence implementation based on GMM (Gauss mixture model) background modeling
GMM
- 使用背景减进行前景提取，并进行人群密度检测-Foreground extracted using background subtraction, and crowd density detection
GMM
- 高斯混合模型，模式识别。matlab源码实现分类-Gaussian mixture model of pattern recognition.The matlab source code to achieve
GMM
- 高斯混合模型算法流程，一组测试数据进行分类，用osg显示-Gaussian mixture model algorithm process, a set of test data classification, using osg display
master2
- 基于GMM-HMM的语音识别程序,自带训练和测试数据，使用的是MATLAB。-GMM-HMM-based speech recognition program, comes with the training and test data, using MATLAB.
pxc3879438
- An automatic speech recognition system for Digits in Malayalam Language is implemented using MFCC and GMM.
EmGMM
- 高斯混合模型的最大期望迭代求解算法,可用于图像区域灰度分布估计-Expectation maximazation(EM) for Gaussian mixture model(GMM)
Hierarchical-clustering
- 里面有层次聚类，k-means和gmm聚类算法-Hierarchical clustering
GMM
- 这个混合高斯目标检测算法的c++代码，并对一组序列进行检测。-This hybrid Gauss target detection algorithm c++ code, and a set of sequences to detect.
hde code
- hde code for speech recognification using the HMM and GMM
GMM
- 聚类算法的方法，用网上的代码，运行完了使用的-Clustering algorithm, using the code on the Internet, run over the use of the
GMM
- 混合高斯背景建模,检测效果很好，拖尾现象几乎没有。-background modeling
mixBern
- Just like EM of Gaussian Mixture Model, this is the EM algorithm for fitting Bernoulli Mixture Model. GMM is useful for clustering real value data. However, for binary data (such as bag of word feature) Bernoulli Mixture is more suitable.
ghsc267762131214061
- gaussian-mixture-models: This tool clusters the input image into n number of colored sections by synthesizing a Gaussian Mixture Model (GMM) using Expectation Maximization (EM).
matlab_gmm
- 针对单高斯建模的不足，提出高斯混合背景模型，可以检测出比较清楚的运动目标，经实验对比，噪声较少
emgmm
- em algorithm for g-em algorithm for gmm
GMM-GMR2
- gaussian mixture model2
EM_GMM-master
- em-gmm-master good for gmm algorithm