Handbook-of-Machine-Vision

利用contourlet变换、小波变换等方法，进行图像去噪，经过调试，完全可以运行，效果不错(Contourlet transform, wavelet transform methods for image denoising, debugging, can run, good results)

利用数字图像处理软件halocn来进行缺陷检测，附带有之前做工程项目的原图源代码，还有ppt和实验指导书(Halocn using digital image processing software for defect detection, comes with artwork done before the project source code, as well as ppt and experimental instructions)

基于图割的图像分割的实现代码，在linux下测试通过(efficient graph cut source code，run in linux)

对视频序列图像的压缩编码，采用了运动预测以增大压缩率(video image sequence coding used to forecast the movement increased compression rate)

针对10个手写数字的识别问题，设计了一个BP神经网络，使它能够正确识别10个数字。(Against the 10 handwritten numeral recognition problem, a BP neural network is designed so that it can correctly identify the 10 digits.)

形状上下文算法用于图像检索，程序中已运用了.mat文件存储图像库中的图像特征，检索效果很好。(shapecontext is used to realize imgage retrival,it shows very effective performance.)

matlab源文件，可以实现利用matlab来画各种饼图，显示直观，对做数据分析和论文的同志会有帮助。(matlab source files, can be achieved using matlab to draw a variety of pie, intuitive display, and to do data analysis and papers comrades would help.)

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.)

KMEANS代码段，用于图像分割，已配置完成(KMEANS,use to achieve image segmentation)

一些常用的图像处理评价指标，处理后图像对比度，边缘剩余数，处理前后图像的相似性....(Some commonly used image processing evaluation, contrast, edges remaining number of similarities ....)