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图像处理中的一些常用代码，包括：腐蚀操作 开启和闭合操作 形态学边界提取 形态学骨架提取 直接提取四个顶点坐标(Some of the commonly used image processing code, including: Opening and closing operation of corrosion morphology boundary extraction morphological skeleton extraction to extract four vertex coordinates)

自己写的Normalized Cut图像分割，聚类程序，程序非常清晰，花了很长时间调试。(Normalize Cut)

说明：  经典图像处理方法之一的图像去雾算法matlab实现(Matlab implementation of image defogging algorithm, one of the classical image processing methods)

利用四参数随机生成法生成三维多孔介质，以研究黏土、页岩等的微观流动机制(Using QSGS to construct 3D porous media)

混合高斯(Mixture of Gaussian, MOG) 背景建模算法和Codebook 背景建模算法被广泛应用于监控视频的运动目标检测问题, 但混合高斯的球体模型通常假设RGB 三个分量是独立的, Codebook 的圆柱体模型假设背景像素值在圆柱体内均匀分布且背景亮度值变化方向指向坐标原点, 这些假设使得模型对背景的描述能力下降. 本文提出了一种椭球体背景模型, 该模型克服了混合高斯球体模型和Codebook 圆柱体模型假设的局限性, 同时利用主成分分析(Principal components analysis, PCA) 方法来刻画椭球体背景模型, 提出了一种基于主成分分析的Codebook 背景建模算法. 实验表明, 本文算法不仅能够更准确地描述背景像素值在RGB 空间中的分布特征, 而且具有良好的鲁棒性.(The background modeling algorithm of mixture of Gaussian (MOG) and codebook is widely used in moving object detection of surveillance video. However, the ball model of MOG usually assumes that the three components of RGB are independent, while the cylinder model of codebook assumes that the value of background pixel is distributed uniformly within the cylinder and the changing direction of brightness points to the origin of the coordinate system. These assumptions reduce the descriptive capability for background modeling. Therefore, the paper proposes an ellipsoid- based background model, which overcomes the MOG and codebook0s limitations. By using principal component analysis to depict the ellipsoid background model, a novel PCA-based codebook background modeling algorithm is proposed. Experiments show that this algorithm can not only give more accurate description of the distribution of background pixels but also have a better robustness.)

需安装Halcon

蚁群算法，图像边缘检测，用于模式识别，可以编译通过！(Ant colony algorithm for image edge detection for pattern recognition can be compiled by!)

说明：  基于场景的RLS的非均匀校正算法，不同均值滤波器比较。(Scene based RLS nonuniformity correction algorithm, different mean filter comparison.)

全变分去噪 J=tv(I,iter,dt,ep,lam,I0) 输入: I - 灰度图像, iter - 迭代次数 [1], dt - 时间步长 [0.2], ep - 提升参数epsilon [1], lam - 保真项 lambda [0], I0 - 输入噪声图像 [I0=I] ([]中的是缺省值)( 全变分去噪 J=tv(I,iter,dt,ep,lam,I0) 输入: I - 灰度图像, iter- 迭代次数 [1], dt - 时间步长 [0.2], ep - 提升参数epsilon [1], lam - 保真项 lambda [0], I0 - 输入噪声图像 [I0=I] ([]中的是缺省值))

极化SAR图像多纹理最大似然处理滤波算法，可以生成处理后的四个通道图像数据。(Multi-polarization SAR image texture filtering deal with maximum-likelihood algorithm, can be generated after the deal with the four-channel image data.)