Description: 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 Hough transforms
* Image-based lighting for illuminating scenes and objects with real-world light images
* Content-based image retrieval, covering queries, representation, indexing, search, learning, and more
* Face detection, alignment, and recognition--with new solutions for key challenges
* Perceptual interfaces for integrating vision, speech, and haptic modalities
* Development with the Open Source Computer Vision Library (OpenCV)
* The new SAI framework and patterns for architecting computer vision applications Platform: |
Size: 12192309 |
Author:kankan |
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Description: 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 Hough transforms
* Image-based lighting for illuminating scenes and objects with real-world light images
* Content-based image retrieval, covering queries, representation, indexing, search, learning, and more
* Face detection, alignment, and recognition--with new solutions for key challenges
* Perceptual interfaces for integrating vision, speech, and haptic modalities
* Development with the Open Source Computer Vision Library (OpenCV)
* The new SAI framework and patterns for architecting computer vision applications-Easy to introduce the latest developments in computer vision. Include:* 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 Hough transforms* Image-based lighting for illuminating scenes and objects with real-world light images* Content-based image retrieval, covering queries, representation, indexing, search, learning, and more* Face detection, alignment, and recognition- with new solutions for key challenges* Perceptual interfaces for integrating vision, speech, and haptic modalities* Development with the Open Source Computer Vision Library (OpenCV)* The new SAI framework and patterns for architecting computer vision applications Platform: |
Size: 12191744 |
Author:kankan |
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Description: 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 regression (RPCR), partial least squares (RSIMPLS) and classi¯ cation
(RDA). Only a few of these methods will be highlighted in this paper. The toolbox
also provides many graphical tools to detect and classify the outliers. The use of these
features will be explained and demonstrated through the analysis of some real data
sets. Platform: |
Size: 294912 |
Author:王一 |
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Description: 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 distribution of the above extreme values (Outliers) statistical methods, the concept of using a linear regression, meaning that he used the covariance matrix and the distribution of the information to find the extreme values of the average production of , but can allow a group of information systems with the robustness (Robust), removing unnecessary noise message here get in front of the data covariance matrix, for example, and added two points, coordinates of vectors to do comparison of Mahalanobis distance Platform: |
Size: 1024 |
Author:nip |
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Description: 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 KNN, SVM, GLM, robust regression and decision trees.
+ Wrappers for RMSE, MAD and misclassification loss functions. Platform: |
Size: 3072 |
Author:milk |
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Description: 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 training samples, and
the representation fi delity is measured by the � 2-norm or
� 1-norm of coding residual. Such a sparse coding model
actually assumes that the coding residual follows Gaus-
sian or Laplacian distribution, which may not be accurate
enough to describe the coding errors in practice. In this
paper, we propose a new scheme, namely the robust sparse
coding (RSC), by modeling the sparse coding as a sparsity-
constrained robust regression problem. The RSC seeks for
the MLE (maximum likelihood estimation) solution of the
sparse coding problem, and it is much more robust to out-
liers (e.g., occlusions, corruptions, etc.) than SRC. An
effi cient iteratively reweighted sparse coding algorithm is
proposed to solve the RSC model. Extensive Platform: |
Size: 1216512 |
Author:刘大明 |
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Description: 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 coding) based classification (SRC) has been successfully used in face recognition. In SRC, the testing image is represented as a sparse linear combination of the training samples, and the representation fidelity is measured by the 𝑙 2-norm or 𝑙 1-norm of coding residual. Such a sparse coding model actually assumes that the coding residual follows Gaussian or Laplacian distribution, which may not be accurate enough to describe the coding errors in practice. In this paper, we propose a new scheme, namely the robust sparse coding (RSC), by modeling the sparse coding as a sparsityconstrained
robust regression problem. The RSC seeks for the MLE (maximum likelihood estimation) solution of the
sparse coding problem, and it is much more robust to outliers (e.g., occlusions, corruptions, etc.) than SRC. An efficient iteratively reweighted sparse coding algorithm is
proposed to solve the RSC model. Platform: |
Size: 18704384 |
Author:徐波 |
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Description: 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 regression example
Feature selection example
Common slope problem example Platform: |
Size: 13312 |
Author:文泽枫 |
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Description: Robust Regression:Weighted Least Squares Regression. The objective function can be sovled by LM method. Platform: |
Size: 2702336 |
Author:L |
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Description: 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 Platform: |
Size: 6144 |
Author:wind |
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Description: 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, ROBPCA), principal component regression (RPCR), partial least squares (RSIMPLS) and classification
(RDA). Only a few of these methods will be highlighted in this paper. The toolbox also provides many graphical tools to detect and classify
the outliers. The use of these features will be explained and demonstrated through the analysis of some real data sets. Platform: |
Size: 429056 |
Author:杨李 |
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Description: 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]. To denoise a pixel, the proposed filter computes a locally
adaptive set of weights and window sizes, which can be proven
to be optimal in the context of non-parametric estimation using
kernels. While we do not report analytical results on the statistical
efficiency of the proposed method in this paper, we will discuss
its derivation, and experimentally demonstrate its effectiveness
against competing techniques at low SNR and on real noisy data. Platform: |
Size: 857088 |
Author:ionutmirel |
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Description: 回归分析，包括线性和非线性回归分析程序以及强健性检验-Regression analysis, including linear and nonlinear regression analysis program and robust test Platform: |
Size: 7168 |
Author:kuaile |
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Search - robust regression