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Description: 利用GMM算法，来检测纹理的一个代码！非常有用.-using GMM to detection texture
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Size: 2048 |
Author: xiongxia |
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Description: Speaker recognition is the task of validating individual s identity using invariant features extracted from their voices print. Speaker recognition technology common applications include authentication, surveillance and forensic applications. This Paper investigates the performance of three automatic model selections based on Gaussian Mixture Model (GMM). These approaches are Bayesian information criterion (BIC), Bayesian Ying–Yang harmony empirical learning criterion (BYY-HEC) and Bayesian Ying–Yang harmony data smoothing learning criterion (BYY-HDS). Experimental evaluation of these methods is presented.
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Size: 243712 |
Author: ZCEEE |
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Description: when you want to read some parameters from the HTK macro you trained, just use this source code. it help you read a htk parameter(macro file). But, it can read all parameter type. it just read GMM parameter(1 state, N mixture)
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Size: 121856 |
Author: whchoi/GodDog |
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Description: C++和opencv相结合的关于图像中高斯建模的程序。-C++ and opencv combination of Gaussian model on the image of the procedure.
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Size: 2808832 |
Author: hehe |
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Description: 求高斯混合模型的EM算法，matlab程序。-Seeking EM algorithm for Gaussian mixture model, matlab program.
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Size: 13312 |
Author: 猛 |
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Description: 混合高斯模型做的视频跟踪系统，具有良好的跟踪效果-Gaussian mixture model to do a video tracking system, has a good tracking results
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Size: 436224 |
Author: 张学贺 |
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Description: GMM GMM高斯混合模型聚类
Gaussian mixture model clustering-GMM GMM Gaussian mixture model clustering
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Size: 5828608 |
Author: 哇阿災 |
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Description: 建立了混合高斯模型，可以对静止背景下运动目标进行检测。-Gaussian mixture model is established, you can still detect moving target in the background.
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Size: 1024 |
Author: lixiang |
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Description: 基于码书的运动目标检测是和混合高斯模型（MoG，GMM）类似的而简单有效的背景剪除方法，附件是VC++6.0编写的基于码书的运动目标检测，可直接读取摄像头，也可改为读取硬盘视频文件，需安装Opencv1.1-Codebook-based moving target detection and Gaussian mixture model (MoG, GMM) and a similar cut off the background simple and effective method of attachment is written in VC++6.0 code book based on motion detection, the camera can be directly read can also be changed to read the hard disk video files to be installed Opencv1.1
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Size: 2741248 |
Author: 梁浩 |
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Description: 一篇详细的介绍高斯混合模型（GMM）参数优化及实现的文档，有实例, 包括VC及matlab 实现。初始学者一看就能懂-A detailed description of Gaussian mixture model (GMM) parameter optimization, and implementation documentation, including the VC and the matlab implementation
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Size: 138240 |
Author: 龚勋 |
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Description: E-step and M-step in G-E-step and M-step in GMM
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Size: 2048 |
Author: sane |
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Description: background subtraction using g-background subtraction using gmm
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Size: 3072 |
Author: jagan |
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Description: em algorithm used in G-em algorithm used in GMM
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Size: 51200 |
Author: 郑良 |
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Description: sparse Bayesain learning algorithms
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Size: 19456 |
Author: lucasschen |
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Description: This project describes the work done on the development of an audio segmentation and classification system. Many existing works on audio classification deal with the problem of classifying known homogeneous audio segments. In this work, audio recordings are divided into acoustically similar regions and classified into basic audio types such as speech, music or silence. Audio features used in this project include Mel Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate and Short Term Energy (STE). These features were extracted from audio files that were stored in a WAV format. Possible use of features, which are extracted directly from MPEG audio files, is also considered. Statistical based methods are used to segment and classify audio signals using these features. The classification methods used include the General Mixture Model (GMM) and the k- Nearest Neighbour (k-NN) algorithms. It is shown that the system implemented achieves an accuracy rate of more than 95 for discrete audio classification.-This project describes the work done on the development of an audio segmentation and classification system. Many existing works on audio classification deal with the problem of classifying known homogeneous audio segments. In this work, audio recordings are divided into acoustically similar regions and classified into basic audio types such as speech, music or silence. Audio features used in this project include Mel Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate and Short Term Energy (STE). These features were extracted from audio files that were stored in a WAV format. Possible use of features, which are extracted directly from MPEG audio files, is also considered. Statistical based methods are used to segment and classify audio signals using these features. The classification methods used include the General Mixture Model (GMM) and the k- Nearest Neighbour (k-NN) algorithms. It is shown that the system implemented achieves an accuracy rate of more than 95 for discrete audio classification.
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Size: 653312 |
Author: kvga |
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Description: 基于EM算法的模型聚类的研究及应用，GMM高斯混合模型-EM-based clustering algorithm and its application model, GMM Gaussian mixture model
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Size: 3404800 |
Author: 思轩 |
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Description: Speech Proceesing Gmm algo
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Size: 1648640 |
Author: Augustine |
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Description: A review on background subtraction in a fixed background video surveillance. It includes basic frame difference method , averaging and gmm based subtraction of background.
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Size: 281600 |
Author: suresh |
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Description: This is gaussian background mode document.
This is adaptp GMM.
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Size: 242688 |
Author: kildong |
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Description: gaussian mixture model
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Size: 3072 |
Author: Iswarya sudhakar |
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