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matlab环境下开发的高斯混合模型，并实现聚类，最佳的类数由最大描述准则(MDL)来确定，并附有实验数据，是国外论文中的代码。-Gaussian mixture model developed in the Matlab environment , and clustering, to determine the optimal number of classes by the largest describe the criteria (MDL) , together with experi
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混合高斯模型原理，以及OpenCV实现的源代码，以及注释-Gaussian mixture model principle, and the realization of the source code
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matlab中可以运行的混合高斯模型，用于运动检测，提供了一个基本的程序框架，各种改进算法可以基于本程序进行-matlab can run Gaussian mixture model for motion detection, provides a basic framework of the program, various improvements to the algorithm based on the Program.
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EM算法用于高斯混合模型，实现数据的精确分类-The EM algorithm for Gaussian mixture model, the exact classification of the data
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实现了高斯混合尺度模型（GSM）算法，可被嵌入到各种去噪框架中（如各种小波变换等）使用。-Gaussian mixture scale model (GSM) algorithm, can be embedded into a variety of denoising framework (such as a variety of wavelet transform, etc.) use.
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利用GMM算法实现语音信号的高斯混合模型的建立-Gaussian mixture model GMM algorithm of speech signals
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利用EM算法实现高斯混合模型的优化，完成特征建模-Use of EM Algorithm to to achieve the the the optimization of of the Gaussian mixture model, to complete the Feature Modeling
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基于高斯混合运动背景模型的运动目标检测和跟踪源程序-Moving target detection and tracking the source of the background model based on Gaussian mixture motion
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为了提高非结构化道路识别算法的有效性,提出了一种道路分割的新方法,建立了道路区域域和非道路区域混合高斯彩色模型,根据像素隶属于彩色模型的概率进行基于彩色信息道路 -
-A road split in order to improve the effectiveness of unstructured road recognition algorithm is proposed to establish a road regional domain and non-road area Gau
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通过gaussian mixture Model 产生一组点， 通过 Expectation Maximation方法对这些等进行划分。 结果表明，此算法能够很好的确认gaussian mixture model。-Gaussian mixture Model is used to produce a set of points. And through Expecation Maximation Method the points will be classified. The results
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这是一篇详细介绍人脸检测与语音驱动口型的文章，其中使使用了高斯混合模型采取了无监督聚类的方法，希望对你有用。
-This is a detailed introducer face detection and voice-driven lip article, which makes use of a Gaussian mixture model to take unsupervised clustering methods, hope useful for you.
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本混合高斯模型是一种基于opencv的，运用用了系统自带的检测函数，检测效果较好.欢迎切磋!
-Gaussian mixture model is based on the opencv, the use of the system comes with the detection functions, better detection. Welcome to learn!
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基于隐马尔可夫模型和高斯混合模型结合的声音转换方法.pdf-Combination of hidden Markov models and Gaussian mixture model-based voice conversion method. Pdf
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该系统能够识别连续说出的数字串和若干组姓名。建模是针对子词(sub-word, eg. 音
素)，具有一定的可扩充性。当加入一个新名字时，只需修改发音词典和任务语法即可。模
型为连续混合高斯输出，运用语音决策树聚类形成的绑定状态式三音素。-The system is able to recognize continuously spoken digit string and the number of groups name. Modeling is a sub-word (sub-wo
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混合高斯背景建模C++源程序
在进行前景检测前，先对背景进行训练，对图像中每个背景采用一个混合高斯模型进行模拟，每个背景的混合高斯的个数可以自适应。-Before the the mixed Gaussian background the modeling C++ source. During foreground detection, background training, each background image using a Gaussian mixture model simu
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针对视频序列运动目标检测，用vc++实现快速高斯混合建模进行前景背景的分离。-In motion object detection of video sequence, with vc++ realize rapid gaussian mixture model carries on the separation between foreward and background.
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Traditional single particle reconstruction methods use either the Fourier or the
delta function basis to represent the particle density map. We propose a more
flexible algorithm that adaptively chooses the basis based on the data. Because
the b
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使用高斯混合模型（GMM），对声音信号进行识别，包括对特征参数的采集、训练和识别过程-Using the Gaussian mixture model (GMM)to realize the recognition of the sound signal, including the acquisition of the characteristic parameters,the training and the recognition process
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混合高斯模型的背景减除法和相邻帧差法相结合进行目标检测的程序，内附视频-The background of the gaussian mixture model reduction division and adjacent frame differential method for target detection with the program, enclosing video
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基于混合高斯模型的算法，我们可以利用该算法提取前景，检测背景！-Gaussian mixture model-based algorithm, we can take advantage of the the algorithm extracted prospects, detect background!
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