GraphExample

这是计算机图形学中的对二维图形进行基本几何变换的程序(This is a two-dimensional computer graphics in the graphics program of basic geometric transformations)

完成机器视觉里反透视变换功能，可以确定敢兴趣区，确定图像到实际道路的分辨率(completion of the anti-machine vision Lane perspective transformation function, it is possible to identify am interested in the district to determine the actual images of the road resolution)

自己编写的C++小程序，十字算法，用到KD树，能够有效进行点云去噪，压缩数据(I have written a small program C++, cross algorithm used in KD tree, the point cloud can be effectively de-noising, compression of data)

这是DCT的改进算法，可以提高DCT算法的执行速度。(This is the DCT of the improved algorithm can improve the execution speed DCT algorithms.)

盲去卷积复原算法，并在MATLAB中进行实验，和维纳滤波的复原效果进行一个对比。盲去卷积的方法有多种，露西-理查德森算法属于图像复原中的非线性算法，与维纳滤波这种较为直接的算法不同，该算法使用非线性迭代技术，在计算量、性能方面都有了一定提升(The blind deconvolution restoration algorithm is carried out in MATLAB and is compared with the restoration effect of Wiener filtering. There are many ways of blind deconvolution. Lucy Richardson algorithm is a nonlinear algorithm in image restoration. Unlike Wiener filtering, this algorithm uses nonlinear iteration technology, and has a certain improvement in computation and performance.)

图像噪点检测，去除难以区分的斑点噪声及平滑图像 双曲型差分(Image denoising and hyperbolic difference)

点云三维重建中需要用的法向量计算，能够有效避免法向量不一致的问题，更好进行三维重建。测试有用。(Normal vector calculation point cloud reconstruction in need, and can effectively avoid the problem of inconsistent normals, better three-dimensional reconstruction. Useful for testing.)

说明：  FMCW信号处理是通过python脚本实现的，可以总结为： FMCW IF信号预处理 范围-时间地图构造（RTM） 多普勒时间地图构造（DTM） 角度时间地图构造（ATM）(FMCW signal process was implemented by python script, which can be sum up as: FMCW IF signal pre-process Range-Time-Map construction (RTM) Doppler-Time-Map construction (DTM) Angle-Time-Map construction (ATM))

说明：  实现图像精确分割，基于ALpha Matting 的基础上的Sharedmatting图像分割算法(Accurate image segmentation,Sharedmatting image segmentation algorithm based on ALpha Matting)

运用pca算法降维，提取主要特征值，从而达到降维目的(Dimensionality reduction using pca algorithm, extract the main features of the value of)