用于unity的6面天空盒子使用方法： 1、Project属性栏 - Creat - 创建材质球Material -命名为Sky 2、右边材质球属性面板中（Inspector）- Shader下来菜单中选择 - Skybox - 6 Sided 3、在Lighting属性面板中 - Environment - Skybox Materail点击右侧小点点，选中我们创建的Sky球 4、注意事项：在Project属性栏里，选中所有导入的6张图片，右边Inspector面板中 Advanced - Fliter Mode - 选择Clamp！！！ 要是默认的Repeat，天空

通过移动的最小二乘法改变和自定义的控制点操作图片。Image Deformation Using Moving Least Squares 移动最小二乘法 图像变形（matlab实现）

Image Deformation Using Moving Least Squares 算法的matlab实现。通过移动的最小二乘法改变和自定义的控制点操作图片。

PCL版本为1.6.0 all in one IDE为 VS2010 本demo实现了mls的用法，压缩包里有点云样本，成功实现了点云的Upsampling。

使用C#对等高线图算法（Marching squares）的完整实现代码。

Matlab下用最小二乘法实现椭圆拟合，适合初学者，希望对大家有帮助！

This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing sparseness and employing robust statistics. The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nyström sampling with active selection of support vectors. The methods are illustrated with several examples.