计算机视觉Github开源论文

使用说明请看这里：https://blog.csdn.net/yj13811596648/article/details/88746350

这是part2 。使用说明看这里：https://blog.csdn.net/yj13811596648/article/details/88746350

% This folder contains a collection of "fitting" functions. % (Some has demo options - the third section) % The GENERAL input to the functions should be samples of the distribution. % % for example, if we are to fit a normal distribution ('gaussian') with a mean "u" and varaince "sig"^2 % then the samples will distribute like: % samples = randn(1,10000)*sig + u % %fitting with Least-Squares is done on the histogram of the samples. % fitting with Maximum likelihood is done directly on the samples. % % % Contents of this folder % ======================= % 1) Maximum likelihood estimators % 2) Least squares estimators % 3) EM algorithm for estimation of multivariant gaussian distribution (mixed gaussians) % 4) added folders: Create - which create samples for the EM algorithm test % Plot - used to plot each of the distributions (parametric plot) % % % % % % Maximum likelihood estimators % ============================= % fit_ML_maxwell - fit maxwellian distribution % fit_ML_rayleigh - fit rayleigh distribution % (which is for example: sqrt(abs(randn)^2+abs(randn)^2)) % fit_ML_laplace - fit laplace distribution % fit_ML_log_normal- fit log-normal distribution % fit_ML_normal - fit normal (gaussian) distribution % % NOTE: all estimators are efficient estimators. for this reason, the distribution % might be written in a different way, for example, the "Rayleigh" distribution % is given with a parameter "s" and not "s^2". % % % least squares estimators % ========================= % fit_maxwell_pdf - fits a given curve of a maxwellian distribution % fit_rayleigh_pdf - fits a given curve of a rayleigh distribution % % NOTE: these fit function are used on a histogram output which is like a sampled % distribution function. the given curve MUST be normalized, since the estimator % is trying to fit a normalized distribution function. % % % % % Multivariant Gaussian distribution % ================================== % for demo of 1

利用多维高斯混合模型，建立背景，然后通过减背景获得前景区域，多维高斯混合模型具有较强的抗噪声，较好适应光线变化

Dirichlet Process Mixture Models(DPMM)的Matlab程序

GMM算法经典论文，opencv中的GMM算法应该就是参考这边文章。自己做的中文翻译pdf也附在了里面

高斯混合模型的matlab源代码，拭一款经典的程序-Gaussian mixture model matlab source code, taking a classic procedure

压缩包里有三个matlab程序，分别是K-mean聚类、LVQ聚类和混合高斯聚类，数据为两类二维高斯分布的随机点。程序展示了三种聚类算法的基本使用方法。算法原理可以参考周志华《机器学习》的第九章聚类。

Improved adaptive Gausian mixture model for background subtraction，经典的GMM算法改进论文，opencv中的GMM改进算法有参考这篇文章，把自己的翻译也附在里面