高斯混合模型

通过EM算法优化估计高斯混合模型的参数，得到了确定的高斯混合模型，从而实现对电梯交通流的在线预测。

The EM algorithm is utilized to estimate the parameters of GMM to predict elevator traffic flow on line .

基于高斯混合模型和Renyi熵的图像分割方法

Image segmentation approach based on GMM and Renyi entropy

多元高斯混合模型脑MR图像去壳模型

Brain MR images skull stripping model based on multi-Gaussian mixture model

基于改进的高斯混合模型脑MR图像分割

The brain MR image segmentation based on an improved Gaussian mixed mode

基于高斯混合模型的脑部MR图像自动分割

Auto Segmentation of Brain MR Image Based on Gaussian Mixture Model Diffusion

基于多元信息的高斯混合模型左心室MR图像分割

MR Image Segmentation of Left Ventricle Based on the Multi-information Gaussian Mixture Model

基于高斯混合模型的人脑MR图像分割新算法研究

On new segmentation algorithm of brain magnetic resonance images based on Gaussian mixture models

高斯混合模型改进的活动轮廓模型MRI分割

MRI Segmentation via Active Contour Model Improved with Gaussian Mixture Model

基于多尺度小波变换的高斯混合模型SAR图像去噪

SAR Speckle Reduction Based on Multi-Scale Wavelets and Gauss Mixture Model

基于高斯混合模型的EM学习算法

A Study of EM Learning Algorithm Based on Gaussian Mixture Model

高斯混合模型聚类中EM算法及初始化的研究

Algorithm EM and Its Initialization in Gaussian - Mixture-Model Based Clustering

利用高斯混合模型的SAR图像目标CFAR检测新方法

A Novel CFAR Algorithm for Detecting Targets in SAR Images Using Gaussian Mixture Model

运用EM算法来实现高斯混合模型的聚类，如何初始化EM参数是一个关键的问题。

When EM algorithm is utilized to realize Gaussian-Mixture-Model based clustering , how to initialize it becomes a pivotal issue .

基于复高斯混合模型的鲁棒VAD算法

Robust Voice Activity Detection Algorithm Based on Complex Gaussian Mixture Model

本文综合考虑了基于内容的图像检索及SAR图像的特点提出了基于高斯混合模型分类的SAR图像检索方法。

This paper considers the characteristic of content-based image retrieval ( CBIR ) and SAR image together , proposing a method of SAR image retrieval .

实现了一个基于高斯混合模型（GMM）的说话人辨识系统。

This paper realized a speaker recognition system based on Gaussian Mixture Models ( GMM ) .

当噪声程度达到9%时，K均值法、模糊C均值法和高斯混合模型的分割准确率平均下降了4%。

When the noise is severe such as 9 % , the segmentation accuracy of K-means clustering algorithm , fuzzy C means and method based on Gaussian mixture model decrease 4 % averagely .

基于高斯混合模型的两级Mel弯曲维纳滤波。

GMM-based two-stage Mel-warped Wiener filter .

本文采用Gabor滤波器的输出结果作为特征，高斯混合模型（GMM）作为分类器对织物瑕疵分类。

We adopt the output of Gabor filters as feature and Gaussian mixture model ( GMM ) as the classifier .

利用高斯混合模型（GMM）由窄带语音的LSF参数扩展得到高带语音的包络谱信息；

Spectrum envelope of high-band speech is obtained from the LSF parameters of narrow-band through GMM .

在众多的说话人识别技术中，本文主要研究了基于Mel频率倒谱系数（Mel-FrequencyCepstrumCoefficients，简称MFCC）和高斯混合模型（Gaussianmixturemodel，简称为GMM）的说话人识别系统。

This paper focuses on the speaker recognition system based on Mel-Frequency Cepstrum Coefficients ( MFCC ) and Gaussian Mixture Model ( GMM ) .

高斯混合模型（Gaussianmixturemodels，GMM）是一种重要的机器学习方法，其目的是建立目标数据集的概率模型。

Gaussian mixture model ( Gaussian Mixture Models , GMM ) is an important method of machine learning , which aims to establish a probability model for the target data set .

在算法方面，高斯混合模型（GMM）是目前最成功的一种说话人识别模型。

In algorithm ways , Gaussian mixture model ( GMM ) is the most successful speaker recognition model at present .

通过高斯混合模型拟合图像直方图，使用EM算法估计高斯参数，进而将图像分割成区域。

This paper will use Gaussian Mixed Model to fit image histogram and EM algorithm to estimate Gaussian parameters , thus to segment an image into some regions .

基于高斯混合模型的NSCT变换遥感图像融合

Gaussian Mixed Model Based NSCT Remote Sensing Image Fusion

说话人识别中有许多先进有效的识别技术，其中高斯混合模型（GMM）由于性能较好、复杂度小、方法简单，是目前最好的说话人识别算法之一。

GMM is one of the best pattern recognition techniques because of its good performance , simpleness and lower degree of complexity .

为了避免直接在多维空间中应用曲线演化模型，采用高斯混合模型（Gaussianmixturemodel，简称GMM）来描述该特征图像的概率分布，再从分布模型中计算得到每个像素点的区域信息和边界信息。

To avoid deforming contours directly in a vector-valued space , a Gaussian mixture model ( GMM ) is used to describe the statistical distribution of the space and get the boundary and region probabilities .

采用高斯混合模型（GMM）来逼近源信号的概率密度函数，简化了算法中的积分，导出了一种实用的期望最大算法（EM）算法迭代式。

Gaussian Mixture Model ( GMM ) is used to approximate the pdf of sources and results in a practical Expectation Maximum ( EM ) algorithm .

采用的方法分别是矢量量化（VQ）识别方法、隐马尔可夫模型（HMM）识别方法、高斯混合模型（GMM）识别方法。

They are Vector Quantization ( VQ ), Hidden Markov Models ( HMM ) and Gaussian Mixture Models ( GMM ) speaker identification respectively .

通过分析GMM（高斯混合模型）的说话人辨认系统的性能，提出了一种捕捉不同说话人交互信息的人工神经网络（ANN）方法，构成一个GMM/ANN混合说话人辨认系统。

An inter-speaker information ANN method is proposed , analyzing GMM model speaker identification system performance , and a hybrid GMM / ANN speaker identification system is structured .