pipei

ZhouZhiqiang2014年发表论文及源码，论文为基于多尺度加权梯度的多聚焦图像融合Multi-scale weighted gradient-based fusion for multi-focus images(Multi-scale weighted gradient-based fusion for multi-focus images)

数字图像处理用到的标准测试图像，很多图片。(IMP test images。)

96个蛮实用的PNG格式图标 仅供编程或图像处理使用。(96 pretty practical PNG format icon super practical!)

利用matlab进行插值法亚像素边缘检测，提取零件轮廓(Subpixel edge contour extraction)

基于图像处理的识别技术，对扑克牌的自动识别，效果很好。(Recognition technology based on image processing,automatic identification of playing cards, the effect is very good. )

三维激光点云配准是点云三维建模的关键问题之一。经典的 ICP 算法对点云初始位置要求较高且配准 效率较低，提出了一种改进的 ICP 点云配准算法。该算法首先利用主成分分析法实现点云的初始配准，获得较好 的点云初始位置，然后在经典 ICP 算法的基础上，采用 k － d tree 结构实现加速搜索，并利用方向向量夹角阈值去除 错误点对，提高算法的效率。实验表明，本算法流程在保证配准精度的前提下，显著提高了配准效率。 (Three-dimensional laser point cloud registration is one of the key three-dimensional point cloud model. High classical ICP algorithm to the initial position of the point cloud registration requirements and low efficiency, proposed an improved ICP point cloud registration algorithm. Firstly, the use of principal component analysis of the initial point cloud registration, get a better initial position of the point cloud, then the basis of classical ICP algorithm using k- d tree structure to achieve speed up the search, and using the direction vector angle the removal of the threshold point error and improve the efficiency of the algorithm. Experiments show that the algorithm processes to ensure the accuracy of registration under the premise, significantly improve the efficiency of registration.)

实现人脸识别功能，GUI界面，其中有测试人脸集，识别率95以上(To achieve face recognition function, GUI interface)

采用多孔trous算法matlab代码，变了好长时间，大家可以参考一下(porous trous algorithm using Matlab code, changed a good long time, we can take a look)

snakes are a variational technique to find the best contour,(Snakes are a variational technique to find the best contour,)

实现Matlab的SRAD(Speckle Reducing Anisotropic Diffusion)算法，用于超声图像的去噪。(Matlab implementation of the SRAD (Speckle Reducing Anisotropic Diffusion) algorithm for ultrasound image denoising.)