• A Gaussian Mixture Model based on color feature is adopted in static layer.

    对于静态背景层,采用基于颜色特征的个数较少的混合高斯模型对背景建模;

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  • 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。

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  • The three Frames subtraction and the background subtraction of Gaussian mixture model are researched.

    重点研究了三帧差分法和混合高斯模型背景差分法。

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  • It is obtained that Gaussian mixture noise can improve the information transmission through the array.

    在高斯混合噪音下计算了单门限系统和门限阵列的互信息量。

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  • This paper presents a method of multi-Gaussian mixture modeling and selecting based HMM speech synthesis.

    本文介绍了一种基于多高斯建模和挑选的HMM语音合成方法。

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  • 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。

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  • 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。

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  • 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。

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  • Gaussian Mixture Density Modelling and Decomposition (GMDD) is a hierarchical clustering method based on robust statistical theory.

    高斯混合密度降解模型(GMDD)是一种基于稳健统计理论的层次聚类方法。

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  • 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。

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  • 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。

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  • 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。

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  • Gaussian Mixture Distribution is introduced into one kind of inhomogeneous Hidden Markov model-simplified Duration Distribution Based HMM in this paper.

    本文将混合高斯分布应用于一种非齐次隐含马尔可夫模型——简化的基于段长分布的隐含马尔可夫模型。

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  • Taking the method of adaptive Gaussian mixture method can make model for background meanwhile it is a difficult point to maintain and update background model.

    采用自适应高斯混合方法为背景建模的难点是对背景模型的维持与更新。

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  • 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。

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  • 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。

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  • This paper proposes an improved background subtraction method based on Gaussian mixture background model which can not deal with the problem of scene light rapid change.

    文中针对混合高斯模型不能应对光线突变的问题,提出了一种改进的背景模型。

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  • 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。

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  • It creates Gaussian Mixture Model for each scenario, and contour of gait is extracted from binary silhouette for Euclidean distance between the centroid and any pixel on it.

    首先建立环境的高斯背景模型,从步态视频序列中提取轮廓图像,计算质心以及轮廓线上的点到质心的欧氏距离。

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  • And if there are no overlaps between each Gaussian component, parameters of Gaussian mixture PDF model can be exact estimated quickly with the dynamic cluster algorithm (DC).

    而在各高斯分量概率密度互不重叠的条件下,使用动态簇算法(DC)则可快速而精确地估计出混合高斯模型参数。

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  • 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。

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  • 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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  • The first, By dividing between consonant and vowel of Chinese syllables, we establish a speech distinction system of Chinese pronouncing accuracy using the continuous Gaussian mixture HMM.

    首先,细化声韵母,对反映普通话发音准确度的声韵过渡段建立连续高斯混合密度HMM的普通话发音标准度评价系。

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  • One-dimensional minimum error thresholding method assumed that the histogram distributions of object and background are governed by a mixture Gaussian distribution.

    一维最小误差阈值法假设了目标和背景的灰度分布服从混合正态分布。

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  • We derive iterative formula of the parameters of a mixture of Gaussian densities.

    推导出高斯混合密度参数的迭代公式。

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  • First we introduce the abstract form of the EM algorithm. Then we develop the EM parameter estimation procedure for one application: finding the parameters of a mixture of Gaussian densities.

    首先给出em算法的抽象形式,然后研究EM参数估计方法的一个应用:求高斯混合密度的参数。

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  • An effective and fast method of face detection for a service robot is proposed, which combines a mixture of Gaussian distribution model of skin tone color with eyes features.

    由于室内存在多种物体,背景不断变化,且光照条件可能不断变化,提出采用人脸肤色的标准混合高斯模型与人眼特征相结合的人脸检测法,无需对原始图像进行尺度变换。

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  • First, we summarize some amS learning algorithms on Gaussian or finite mixture based on the Bayesian Ying-Yang (BYY) harmony learning principle.

    首先,我们综述了基于贝叶斯阴阳机和谐学习原则的自动模型选择学习算法。

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  • First, we summarize some amS learning algorithms on Gaussian or finite mixture based on the Bayesian Ying-Yang (BYY) harmony learning principle.

    首先,我们综述了基于贝叶斯阴阳机和谐学习原则的自动模型选择学习算法。

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