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Emerging.Topics.in.Computer.Vision
- 深入浅出介绍计算机视觉的最新动态。内容包括： * Camera calibration using 3D objects, 2D planes, 1D lines, and self-calibration * Extracting camera motion and scene structure from image sequences * Robust regression for model fitting using M-estimators, RANSAC, and
RSC
- 强壮的人脸识别系统，发表于cvpr2011年，程序是应用matlab实现-Recently the sparse representation (or coding) based classifi cation (SRC) has been successfully used in face recognition. In SRC, the testing image is represented as a sparse linear combination of the trai
LIBRA_19jun09
- Our toolbox currently contains implementations of robust methods for location and scale estimation, covariance estimation (FAST-MCD), regression (FAST- LTS, MCD-regression), principal component analysis (RAPCA, ROBPCA), princi- pal component re
000
- Mahalanobis距離是一個可以準確找出資料分布上面極端值(Outliers)的統計方法,使用線性迴歸的概念,也就是說他使用的是共變數矩陣以及該資料分布的平均數來找尋極端值的產生,而可以讓一群資料系統具有穩健性(Robust),去除不必要的雜訊訊息,這邊拿前面共變數矩陣的資料為例,並且新增了兩個點座標向量來做Mahalanobis距離的比較-Mahalanobis distance is the information that can accurately identify the dis
TomCat
- Excellent code for robust regression and multivariate dataanalysis
TOMCAT
- 强健性多元回归工具箱，强健性PCA，强健性PLS-robust regression toolbox，including robust PCA，Robust PLS
K-Fold_CV_Tool
- MATLAB cross-validation tool for classification and regression v0.1 FEATURES: + K-fold cross validation. + Arbitrary train and prediction functions with parameters can be used. + Arbitrary loss function can be used. + Wrappers for
RSC
- 人脸识别的稀疏表示识别方法将稀疏表示的保真度表示为余项的L2范数，但最大似然估计理论证明这样的假设要求余项服从高斯分布，实际中这样的分布可能并不成立，特别是当测试图像中存在噪声、遮挡和伪装等异常像素，这就导致传统的保真度表达式所构造的稀疏表示模型对上述这些情况缺少足够的鲁棒性。而最大似然稀疏表示识别模型则基于最大似然估计理论，将保真度表达式改写为余项的最大似然分布函数，并将最大似然问题转化为一个加权优化问题-Recently the sparse representation (or codin
Fitting_with_MATLAB_1_
- 检查/剪裁的例子 孤立点检测实例 正交回归的例子 单因数变异数(泊松回归/逻辑回归)的例子 鲁棒回归的例子 特征选择的例子 常见的边坡问题的例子 -Censoring/clipping example Outlier detection example Orthogonal regression example GLM (Poisson regression/logistic regression) example Robust regress
matlab-econometric-toolbox
- “Applied Econometrics using MATLAB”配套的计量经济Matlab包-MATLAB code for: 1. least-squares, simultaneous systems (2SLS,3SLS, SUR) 2. limited dependent variable (logit, probit, tobit) and Bayesian variants 3. time-series (VAR, BVAR, ECM) estim
reweight
- Robust Regression:Weighted Least Squares Regression. The objective function can be sovled by LM method.
robustfit
- 稳健性回归分析拟合，带拟合阶数.属非线性最小二乘法，不受少量异常数据影响-Robust regression fitting with fitting order. Genus nonlinear least-squares method, a small amount of abnormal data is not affected
a-MATLAB-library-for-robust
- 介绍一个稳健性分析工具箱。主要做稳健性主成分、主成分回归、分类。-Our toolbox currently contains implementations of robust methods for location and scale estimation, covariance estimation (FAST-MCD), regression (FAST-LTS, MCD-regression), principal component analysis (RAPCA, ROB
Image-Denoising-by-Adaptive-Kernel-Regression
- This paper introduces an extremely robust adaptive denoising filter in the spatial domain. The filter is based on non-parametric statistical estimation methods, and in particular generalizes an adaptive method proposed earlier by Fukunaga [1]
LSSVM
- 最小二乘支持向量机工具箱1.6版。含稳健回归和贝叶斯推理，功能强大。-LS-SVM Toolbox version 1.6. With robust regression and Bayesian inference, and powerful.
matlab-R2012a-Parametricstatistics
- matlab R2012a源代码（正太分解法计算均值和方差、图方法正太检验、M稳健回归）。-(calculated the mean and variance diagram method, inspection, teenage boy M robust regression teenage boy decomposition method). .
paper5
- Robust regression using iteratively reweighted least-squares -Robust regression using iteratively reweighted least-squares
paper6
- a Note on Computing Robust Regression Estimates Via Iteratively Reweighted
ComputerVision
- 基于投影M估计量的稳健回归方法，用于估计计算机视觉立体像对间的Fundamental Matrix。Fundamental Matrix，参考文献：H. Chen, P. Meer, Robust regression with projection based M-estimators. 9th International Conference on Computer Vision (ICCV), Nice, France, October 2003, 878-885.PS：这是作者11年前本
StatLSSVM
- 支持向量机工具箱By Kris De Brabanter,标准的非参数回归，健壮的回归，一些调优标准等经典交叉验证，较好的交互性-The StatLSSVM toolbox is written so that only a few lines of code are necessary in order to perform standard nonparametric regression, regression with correlated errors and robust regre