搜索资源列表
BLS-GSM
- BLS-GSM代表“Bayesian Least Squares - Gaussian Scale Mixture(贝叶斯最小二乘-高斯尺度混合模型)”。 这个工具箱实现了该篇论文中介绍的算法： J Portilla, V Strela, M Wainwright, E P Simoncelli, Image Denoising using Scale Mixtures of Gaussians in the Wavelet Domain, IEEE Transaction
Video-detect
- 目标自动识别与跟踪系统，基于MFC界面和opencv视觉库，采用混合高斯进行目标识别，基于局部颜色直方图模型进行自动跟踪，并实现跟踪框可变，跟踪结果实时显示。-target detection and track system
BSG_GMM
- 背景检测算法，利用混合高斯模型，可以每一帧更新背景帧得到较好的结果-Background detection algorithm, using Gaussian mixture model, you can update each frame background frame to get better results
Hybrid-Gaussian
- 混合高斯模型，能够区分前景和背景。导入视频可世界运行。但是还没有去噪，可能对前景分析不够准确。-The code of Hybrid Gaussian,It can distinguish the difference of foreground and background.
hybrid-gaussian
- 混合高斯模型，提取前景和背景。效果勉强，可直接使用。-Mixed Gauss model, extraction of foreground and background.
create_mix_gaussian
- 创建一个混合的高斯正太分布图，先初始化，再创建，最后出图-Create a mixture of Gaussian normal distribution chart, the first initialized, create, and finally the plot
create_mix_2D_gaussian
- 创建一个二维混合的高斯正太分布图，先初始化，再创建，最后出图-Create a two-dimensional Gaussian mixture normal distribution chart, the first initialized, create, and finally the plot
beijingxiangjian
- 基于opencv的运动目标检测，采用了混合高斯和背景相减两种方法，带源码，希望有点用-Opencv based moving target detection, using a mixture Gaussian and background subtraction of two ways, with source code, hope Somewhat
GMM_3
- 自己改进的混合高斯代码，废了好几天时间，希望大家有用，共同学习-Improved Gaussian Mixture own codes, learn together
mixGassianFGextract
- 利用混合高斯进行背景建模，以此来提取前景-Use Gaussian mixture background modeling, in order to extract the foreground
run
- 基于混合高斯模型的背景减除实现程序，速度还可以，但是效果不是特别好，还有待改进-Gauss mixture model background subtraction procedure based on the implementation, can also speed, but the effect is not particularly good, there is room for improvement
GMM-GMR-v2.0
- 利用混合高斯模型对背景图像进行建模，所谓“混合高斯”的意思就是每个像素都是由多个单高斯分布混合组成的。-Name of the file which the classifier is loaded. Only the old haar classifier (trained by the haar training application) and NVIDIA s nvbin are supported for HAAR and only new type of OpenCV XML ca
bgfg_egmml
- 采用混合高斯模型对背景进行建模，然后对视频的运动目标进行检测！-By using the gaussian mixture model for background modeling, and then to detect the moving targets of video!
gmm
- EM算法以及混合高斯模型，c++实现，控制台程序，函数调用很简单方便。可以在低版本vc6.0运行。-EM algorithm and hybrid Gauss model, c++ implementation, the console program, function call is very simple and convenient. Can be run at low vc6.0.
npbayes
- 剑桥大学无参数贝叶斯课程的代码,主要包括狄利克雷过程，主题模型，无限混合高斯分布等-code of nonparametric bayesian cambridge， include dirichlet process, topic model, infinite mixutre gaussian
meanshift-tracking
- 本算法实现的是目标的跟踪，采用的是混合高斯模型建立背景，然后用meanshift进行跟踪，包括使用MFC进行界面编辑-The algorithm is to track the target, using Gaussian mixture model background, and then meanshift track, including the use of interfacial MFC edit
Signal-to-Noise-Ratio
- 在仿真实验中要产生具有某个信噪比的混合信号样本zt。 这时，先求出不含噪声的有用信号的幅度(最大值)am 再根据给定的信噪比snr(db)反推噪声电平theta。以高斯噪声为例-In the simulation experiments to produce with a signal-to-noise ratio of zt mixed signal sample.at this moment, to take the first without noise and useful
EM_suanfa
- 基于EM算法的混合高斯分布参数估计方法，包括权值、均值和标准差-Mixed Gaussian distribution parameter estimation method based on the EM algorithm, weights, including the mean and standard deviation
EM
- 对于混合高斯分布的情况，使用最大期望算法，通过不断计算每个样本的均值与方差，使得似然函数达到最大值。可以很好地处理满足一定概率分布的数据。 代码中通过mvnrnd()函数，设定其中的参数，产生符合混合高斯分布的一组数据集。-For the case of a mixed Gaussian distribution, using expectation-maximization algorithm, through continuous calculation of the mean and
EM
- 实验报告，实现：对于混合高斯分布的情况，使用最大期望算法，通过不断计算每个样本的均值与方差，使得似然函数达到最大值。可以很好地处理满足一定概率分布的数据。 代码中通过mvnrnd()函数，设定其中的参数，产生符合混合高斯分布的一组数据集。-Lab reports, to achieve: the case of the mixed Gaussian distribution, using expectation-maximization algorithm, through continuo