A range image segmentation algorithm based on Gaussian mixture model of surface normal is proposed.
提出了一种基于表面法向的高斯混合模型的距离图像分割算法。
In algorithm ways, Gaussian mixture model (GMM) is the most successful speaker recognition model at present.
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。
The effect of Gaussian Mixture model used in training background model is good, but its convergence velocity is low.
混合高斯模型在训练背景模型的过程中效果良好，但其收敛速度较慢。
This paper introduced three skin-color detecting model: Statistical color model, Chroma Space model and Gaussian Mixture model.
本文介绍了常用的三种肤色检测模型：统计颜色模型、色度空间模型和高斯混合模型。
This feature vector made the Gaussian Mixture Model (GMM) classifier outperform MFCC and Differential MFCC features in classification.
该混合特征使得高斯混合模型(GMM)分类器可获得比使用MFCC特征及其差分MFCC更好的分类性能。
Gaussian mixture model is firstly built as the statistical model for the intensity image, with an estimation of index number using MDL.
首先建立高斯混合的灰度统计模型，运用MDL准则自动确定类别的数目。
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。
And then, taking into account the specific process of automatic image annotation, we built the automatic image annotation model based on Gaussian mixture model.
再针对具体的自动标注过程，建立了基于高斯混合模型的自动图像标注模型。
This paper proposes a new method of tracking the moving maritime objects in video sequences based on neighboring information fusion using Gaussian mixture model.
本文提出了一种基于高斯混合模型邻域信息融合的海面运动目标检测算法。
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。
Experimental results show that compared with moving object detection approach based on conventional Gaussian mixture model, it has a desirable stability and learning ability.
实验结果表明，该方法与传统高斯混合背景模型相比，有较好的学习能力与稳定性，能提高运动目标检测的正确率。
In order to improve the robustness of voice activity detection (VAD), the use of an algorithm based on complex Gaussian mixture model under nonstationary noisy environments was presented.
针对语音激活检测的鲁棒性问题，提出在非平稳噪声环境下使用基于复高斯混合模型的鲁棒语音激活检测算法。
In order to improve the robustness of voice activity detection (VAD), the use of an algorithm based on complex Gaussian mixture model under nonstationary noisy environments was presented.
针对语音激活检测的鲁棒性问题，提出在非平稳噪声环境下使用基于复高斯混合模型的鲁棒语音激活检测算法。
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