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Solving Inverse Problems with Piecewise Linear
- A general framework for solving image inverse problems is introduced in this paper. The approach is based on Gaussian mixture models, estimated via a computationally efficient MAP-EM algorithm.
opencv em算法
- Expectation-Maximization The EM (Expectation-Maximization) algorithm estimates the parameters of the multivariate probability density function in a form of the Gaussian mixture distribution with a specified number of mixtures.
人脸检测与语音驱动口型的文章
- 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 introduction Face Detection and voice-driven I-type article, which uses the Gaussian mixture model taken unsupervised clustering method, in the hope that useful to you.
GMM.rar
- 混合高斯建模是背景建模中的一种经典方法，对复杂背景具有较好的适应性！,Gaussian mixture modeling is modeling in the context of a classical method, the complex has better adaptability background!
GMM
- Source code - create Gaussian Mixture Model in following steps: 1, K-means 2, Expectation-Maxximization 3, GMM Notice: All datapoints are generated randomly and you can config in Config.h-Source code- create Gaussian Mixture Model
OnTrackingofMovingObjects
- 学位论文；运动物体跟踪方法主要包括卡尔曼滤波，Mean-shift，Camshifi算法，粒子滤波器，Snake模型等；应用卡尔曼滤波方法设计了一套煤矿矿工出入自动监测系统；提出了一种新的基于高斯混合模型的颜色特征提取方法，该方法克服了现有的Camshift算法Continuousl y Adaptive eanshift中跟踪目标特征提取精确度低和计算复杂度高的缺陷-Dissertation moving object tracking methods include Kalman filt
Baseball_Playfield_Segmentation_Using_Adaptive_Ga
- a document about "Baseball Playfield Segmentation Using Adaptive Gaussian Mixture Models"
Gaussian_Mixture_Models_and_Probabilistic_Decisio
- very good Gaussian Mixture Models and Probabilistic Decision-Based Neural Networks for Pattern Classification - A Comparative Study document -very good Gaussian Mixture Models and Probabilistic Decision-Based Neural Networks for Pattern Classificatio
High
- This paper presents a clustering approach which estimates the specific subspace and the intrinsic dimension of each class. Our approach adapts the Gaussian mixture model framework to high-dimensional data and estimates the parameters which best
detect
- 基于混合高斯建模的运动检测论文包。内含PDF 和NH格式-Gaussian mixture modeling based on motion detection paper bag.
rennian
- 一种基于肤色分割、区域分析和模板分布的彩色图像人脸检测算法.首先对输入的彩色图像利用混合高斯模型和亮度模型进行分割,然后根据人脸五官的结构特征对得到的区域进一步分析处理,获得所有可能的候选人脸.接着构造了一种基于双眼和人脸模板的概率模型并利用其对候选人脸进行最终检测.-Based on skin color segmentation, regional analysis and the template in color images of face detection algorithm. F
DeepShah
- Motion Based Bird Sensing Using Frame Differencing and Gaussian Mixture
Investigation_on_Model_Selection_Criteria_for_Spe
- 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 Pa
modelbasedonspectrumprediction
- 文章展示了基于高斯混合模型的语音频谱预测方法。频谱预测可能在传包过程中预防丢包这方面起到大作用。期望最大化算法用两倍或三倍的连续语音因素来测试模型。模型被用来设计第一，儿等指令预测量。预测表用频谱分配状态来估计并和一个简单的参考模型对比。最好的预测表得到一个平均频率扭曲值是0.46dB小于参考模型-This paper presents methods for speech spectrum prediction based on Gaussian mixture models. Spec
Algorithm-collections-for-digital-signal---E.-S.-
- SUMMARY: The Algorithms such as SVD, Eigen decomposition, Gaussian Mixture Model, HMM etc. are scattered in different fields. There is the need to collect all such algorithms for quick reference. Also there is the need to view such algorithms in ap
stauffer-mog-
- stauffer的经典的混合高斯模型算法描述，适合做行为检测的人使用。-stauffer the classical Gaussian mixture model algorithm descr iption, suitable for people who use behavior detection.
Gaussian-mixture-mode
- 混合脑部图像处理，比较实用，英文论文，模式识别专业论文-Hybrid image processing in the brain, more practical, the English papers
GS
- 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!
Zivkovic04icpr
- Zoran Zivkovic 写的一篇关于混合高斯进行背景检测的改进论文-Zoran Zivkovic wrote an article on the Gaussian mixture background detection improvements papers
Gupta-and-Chen---2010---Theory
- This introduction to the expectation–maximization (EM) algorithm provides an intuitive and mathematically rigorous understanding of EM. Two of the most popular applications of EM are described in detail: estimating Gaussian mixture models (GMMs),