ruihuafudiao

针对opencv的配制方法总结，适合初学者来配置开发环境(For the preparation of opencv summary, suitable for beginners to configure the development environment)

根据D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis 以及 C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision.这两篇文献提供的方法编写的图像分割代码，作者是 Chris M. Christoudias, Bogdan Georgescu，代码经我看了后加了丰富的中文注释，希望可以给各位带来阅读上的方便。 基于meanshift的聚类分割方法包括滤波、区域融合等操作，通过调整sigma和sigmar来调整分割效果。(According to D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis, and C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision. These two documents prepared by the methods provided by image segmentation code , the author is Chris M. Christoudias, Bogdan Georgescu, after I read the code, add a rich Chinese notes, hoping to bring you the convenience of reading. Segmentation method based on clustering meanshift including filtering, regional integration and other operations, and by adjusting the sigma sigmar to adjust segmentation results.)

对256bmp进行图象处理 对256bmp进行图象处理(256bmp carried out on the image processing carried out on 256bmp image processing)

对图像（Bmp）的智能读入界面设计，源代码(Read image (BMP), intelligent interface design, source code)

数字图像处理实验——直方图的均衡化，打开图像并实现其均衡化(Digital image processing experiments- histogram equalization, open the image and realize its equalization)

用VC++视觉检测程序，可以打开PCB板的照片文件，对其进行编辑、点运算、几何变换、正交变换、图像增强、形态变换、边缘与轮 廓检测、图像分析与复原、图像编码等功能。其中点运算包括图像反色、线性变换、阀值变换等功能；边缘检测包括Robert算子、 Sobel算子、Gauss-Laplace算子等；正交变换包括傅里叶变换、离散余弦变换、沃尔什变换；图像编码包括哈弗曼编码表、香农弗 曼编码表、行程编码、LZW编码等。 对于研究电子封装技术，视觉检测及电机精确控制的同学学习参考使用。(visual detecting for PCB.rar)

图形图像正交变换，离散余弦变换，VC++实现。(Graphic image orthogonal transform, discrete cosine transform, VC++ to achieve.)

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.)

说明：  很好用的三维重建软件，VisualSFM软件允许上传一系列图像，它从这些图像中找到每一个图像的特定特征，利用这些特征信息重建出3D模型的稀疏点云，而后还可进行稠密点云重建。(The VisualSFM software, a good 3D reconstruction software, allows uploading a series of images. It finds the specific features of each image from these images, and USES these feature information to reconstruct the sparse point cloud of the 3D model, and then it can reconstruct the dense point cloud.)

基于openCV的gabor函数,一位牛人编写的。希望对大家有用(gabor function based on openCV)