3642112153_2

说明：  ＬＢＭ三维渗流计算，可以导出流场图和无量纲渗流系数(The flow field diagram and dimensionless seepage coefficient can be derived by LBM three-dimensional seepage calculation)

压缩感知的快速Laplace恢复算法,算法适用于超完备字典(the Beyes recovery algorithms are similar to the sparse coding based on the over-complete dictionary. )

说明：  伪Zernike矩特征提取，Zernike矩特征提取，供参考，很有用(Pseudo-Zernike moment feature extraction, Zernike moment feature extraction, for reference, very useful)

混合高斯前景提取。数学建模时编的，测试视频就在包内，可以作为学习交流用，环境2014a的matlab，后有添加形态处理都在注释里。一起学习。有matlab编的vibe程序欢迎交流(Hybrid Gauss foreground extraction. Mathematical modeling of the series, the test video is in the package, can be used as a learning exchange, the environment 2014a MATLAB)

说明：  增广拉格朗日乘子法ALM算法是机器学习中十分常用且有效的一种优化算法，经常用于低秩和稀疏问题的优化求解中，这个包是增广拉格朗日乘子法的matlab代码(ALM algorithm of augmented Lagrange multiplier method is a very common and effective optimization algorithm in machine learning. It is often used in the optimization of low rank and sparse problems. This package is the matlab code of augmented Lagrange multiplier method)

萤火虫算法 GSO算法用matlab编程实现(Firefly algorithm GSO algorithm using matlab programming)

说明：  基于高光谱数据集PaviaU的数据降维与分类,基于核函数的主成分分析kpca测试(Dimension reduction and classification based on hyperspectral data set paviau, KPCA test based on kernel function principal component analysis)

改进的分水岭算法，能够较好地提取对象轮廓、准确得到物体边界的优点。在MATLAB6.5下顺利编译通过(improve the watershed algorithm can be used to extract object contours, to be precise object boundary advantages. In MATLAB6.5 smoothly through the compiler)

说明：  sift算法 matlab语言的实现 包含了具体的代码和完整注释解释(The realization of SIFT algorithm in MATLAB Contains specific code and full annotation explanation)

在逆向工程中通过测量仪器得到的产品外观表面的点数据集合也称之为点云，通常使用三维坐标测量机所得到的点数量比较少点与点的间距也比较大，叫稀疏点云；而使用三维激光扫描仪或照相式扫描仪得到的点云点数量比较大并且比较密集，叫密集点云。(Data collection point in the surface appearance of the product obtained by reverse engineering measurement instruments, also known as point clouds, usually spaced three-dimensional coordinate measuring machine number of points obtained in relatively small dots are relatively large, called sparse point cloud while the number of point cloud using the three-dimensional laser scanner or camera scanner get bigger and more intensive, called dense point clouds.)