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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
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机器学习中的关于逻辑回归（LR）方法的分类器，Matlab源码，附带四个数据集用于实验-Machine Learning on the logistic regression (LR) method of classification, Matlab source code, with four data sets for experiments
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数据分析与统计的相关学习和程序，包括概率和回归分析-Data analysis and statistics related to learning and procedures, including probability and regression analysis, etc.
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支持向量机结构回归算法（svm-struct），一种新的回归方法，有文献和PPT，方便学习。-Support the structural regression algorithm vector machine (svm-struct), a new regression method, literature and PPT to facilitate learning.
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本软件适合工程技术人员及学生进行数据分析。适用范围广、输入界面简单方便、功能模块实用强大、操作简便易懂。
本软件可进行二元线性与非线性相关分析；多元线性与非线性相关分析；多元线性的相关矩阵分析；数理统计与误差分析；计算行列式的值、求解多元方程组；学习与研究函数的图形及特性；计算常用函数等等。例如：可对化验分析成果进行回归及误差分析、对地质物化探数据进行相关性分析、对测量数据进行误差分析、对社会调查数据进行统计分析等等。 -The software for engineering an
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支持向量机回归理论与神经网络等非线性回归理论相比具有许多独特的优点有线性回归和非线性回归,其模型的选 择包括核的选择、容量控制以及损失函数的选择.在控制方面的研究包括非线性 时间序列 的预测及应用、系统辨识以及优化控制和学习控制等方面的研究-Support vector machine (SVM) regression theory and neural network has many unique advantages such as nonlinear regression theory
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This project aims to develop and share fast frequent subgraph mining and graph learning algorithms. Currently we release the frequent subgraph mining package FFSM and later we will include new functions for graph regression and classification package
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Karl Sketting RLS-DL回归最小二乘字典学习算法，可用于超分辨率重建，MATLAB版-Karl Sketting RLS-DL dictionaries least squares regression learning algorithms can be used to super-resolution reconstruction, MATLAB version
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在机器学习领域，支持向量机SVM(Support Vector Machine）是一个有监督的学习模型，通常用来进行模式识别、分类、以及回归分析-In the field of machine learning, support vector machine SVM (Support Vector Machine) is a supervised learning model, typically used for pattern recognition, classification, and
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In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. Given a
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人脸特征点定位，将深度学习运用到人脸对齐中，卷积神经网络
-Facial features localization, depth learning to use face alignment, the convolutional neural network
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非常有效、高准确率的人脸对齐方法，“显式形状回归”。通过训练数据最小化对齐错误函数，学习一个向量回归函数直接推断整个面部形状（一个特征点集合）-Very efficient and highly accurate face alignment method, explicit shape regression . Through the training data minimization alignment error function, learning a vector regressi
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