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- Gaussian mixture density model 高斯混合模型
- Based on finite Gaussian mixture model of the EM algorithm source code, which has run reports and experimental results. 详细说明:基于有限高斯混合模型的EM算法的源程序代码,里面有实验报告和运行结果。
- Then, built the color model for background and foreground utilizing the GMM (Gaussian mixture model). 然后利用高斯混合模型建立其前景和背景区域的颜色模型；
- We use the fixed-arithmetician DSP chip (chipboard) in the DSP, the algorithm of recognition is Gaussian mixture model. DSP方面使用的是定点运算的DSP板，而辨识的演算法是利用高斯混合模型。
- Gaussian mixture model(GMM) has been widely used for text-independent speaker recognition.This method has simple and efficient character. 高斯混合模型(GMM)已广泛地应用于文本无关的说话人识别系统,该方法具有简单高效的特点。
- This dissertation adopts ASR based on Gaussian Mixture Model (GMM), so re-search emphasizes on robust features and speech enhancement techniques. 消除噪声所带来的失配可以映射到信号空间、特征空间和模型空间。
- Gaussian mixture model (GMM) is first trained for grass pixel, the grass distribution is then computed based on the trained GMM. 首先利用高斯混合模型(GMM)对草地颜色建模，然后利用已建立的GMM计算出每个镜头的草地颜色分布特征。
- Introduce a novel feature selection algorithm based on Gaussian mixture model and do clustering on synthetic and real data making use of the results of the algorithm. 摘要介绍了一种新颖的基于高斯混合模型的特征选择算法，并且应用该方法的结果对模拟数据和真实数据进行聚类。
- The experimental results show that the proposed model can effectively cope with the inaccurate updating problem in the Gaussian mixture model due to the fixed update rate. 实验结果表明，该模型能较好地处理混合高斯模型因采用同一更新速率导致的背景模型更新错误问题。
- The traditional training methods of Gaussian Mixture Model(GMM) are sensitive to the initial model parameters,which often leads to a local optimal parameter in practice. 为了解决传统高斯混合模型(GMM)对初值敏感,在实际训练中极易得到局部最优参数的问题,提出了一种采用微粒群算法优化GMM参数的新方法。
- In this paper, a speaker identification system is proposed based on classify Fea-ture Sub-space Gaussian Mixture Model and Neural Net fusion (FS-GMM/NN) . 该文提出了一种基于分类高斯混合模型和神经网络融合(FS-GMM/NN)的说话人识别方法，通过对特征矢量进行聚类分析，将说话人的训练语音分成若干类。
- The former models skin regions with Gaussian mixture model (GMM) and sets up rules for skin detection. 针对GMM中分量数合理选择问题,提出一种基于聚类有效性函数的最优分量数确定方法,以提高肤色检测的精度。
- Probability Density Function Approximation Using Gaussian Mixture Model 利用高斯混合模型实现概率密度函数逼近
- In this paper,two methods based on DCT and Gabor wavelet transform are respectively proposed to extract the features of skin texture. The extracted features are inputted into Gaussian mixture model(GMM)with which the skin texture detection is performed. 该文采用基于DCT变换和Gabor小波变换两种方法进行皮肤纹理的特征提取,提取的特征作为高斯混合模型(GMM)输入向量,然后通过GMM进行皮肤纹理检测。
- We then describe an entropy penalized AMS learning algorithm on Gaussian mixture. 然后，我们描述了另一种基于熵惩罚的自动模型选择学习算法。
- In the current paper, we survey some main results of AMS on Gaussian mixture or general finite mixture. 本文将对于高斯混合模型或一般有限混合体模型的自动模型选择学习算法及其典型应用进行综述与总结。
- Experimental results show that this method can obtain better results than the Gaussian mixture model (GMM) and Fuzzy vector quantization method in the case of the little training data. 实验表明，该方法对于较短的训练语音，其识别效果优于高斯混合模型和模糊矢量量化。
- Additionally, the proposed speaker's location detection method utilizes Gaussian mixture model (GMM) to model a corresponding phase difference distribution for each specific location of the speaker. 本篇论文所提出的语者定位法则利用高斯混合模型来针对每个位置所独具的特徵(相位差分布)作出模型化的动作。
- The author proposes a textured image segmentation algorithm which is based on multiresolution wavelet and Gaussian mixture models. 提出了一个基于多分辨率小波采样和高斯混合模型的纹理图像分割方法。
- For the two-component Gaussian mixture, it is hard to work out the maximum likelihood estimates (MLEs) and hence difficulty in the influence analysis. 摘要对于两高斯混合分布，很难求参数的极大似然估计，当然也不便于影响分析。
