DCTJPEG

施法中新版《计算几何与计算机图形学》，书中所带光盘，包含书中源码和其团队开发的软件，能够实现曲线曲面拟合和编辑(The new cast of Computational Geometry and Computer Graphics , the book brought the disc that contains the source code in the book and his team developed software enables curve and surface fitting and edit)

使用C++和CUDA编写的双边滤波器。在平滑图像的同时可以保持边界(Using bilateral filter the C++ and CUDA written. Boundary can be kept in the smooth image while)

CGAL，Computational Geometry Algorithms Library，计算几何算法库，设计目标是，以C++库的形式，提供方便，高效，可靠的几何算法。CGAL可用于各种需要几何计算的领域，如计算机图形学，科学可视化，计算机辅助设计和建模，地理信息系统，分子生物学，医学成像，机器人运动规划，网格生成，数值方法等等。 　　 计算几何算法库（CGAL），提供计算几何相关的数据结构和算法，诸如三角剖分（2D约束三角剖分及二维和三维Delaunay三角剖分），Voronoi图（二维和三维的点，2D加权Voronoi图，分割Voronoi图等），多边形（布尔操作，偏置），多面体（布尔运算），曲线整理及其应用，网格生成（二维Delaunay网格生成和三维表面和体积网格生成等），几何处理（表面网格简化，细分和参数化等），凸壳算法（2D，3D和dD），搜索结构（近邻搜索，kd树等），插值，形状分析，拟合，距离等。(The goal of the CGAL Open Source Project is to provide easy access to efficient and reliable geometric algorithms in the form of a C++ library. CGAL is used in various areas needing geometric computation, such as: computer graphics, scientific visualization, computer aided design and modeling, geographic information systems, molecular biology, medical imaging, robotics and motion planning, mesh generation, numerical methods... More on the projects using CGAL web page. The Computational Geometry Algorithms Library (CGAL), offers data structures and algorithms like triangulations (2D constrained triangulations, and Delaunay triangulations and periodic triangulations in 2D and 3D), Voronoi diagrams (for 2D and 3D points, 2D additively weighted Voronoi diagrams, and segment Voronoi diagrams), polygons (Boolean operations, offsets, straight skeleton), polyhedra (Boolean operations), arrangements of curves and their applications (2D and 3D envelopes, Minkowski sums), mesh generation (2D Del)

有关三维模型的泊松算法的诸多应用的实现，包括泊松重建和泊松纹理混合算法，由原作者开发，非常可靠(C++ implementation for algorithms including poisson mesh and poisson texture stitching, it is shared by author with wide utilization)

这些代码是有关数字图像处理的一些算法，大家有需要的，可以在里面找些与自己相关的(These codes are some of the digital image processing algorithms, we need, in which you can find some with their associated)

：植物种类识别方法主要是根据叶片低维特征进行自动化鉴定。然而，低维特征不能全面描述叶片信息，识别准确率低，本文提 出一种基于多特征降维的植物叶片识别方法。首先通过数字图像处理技术对植物叶片彩色样本图像进行预处理，获得去除颜色、虫洞、 叶柄和背景的叶片二值图像、灰度图像和纹理图像。然后对二值图像提取几何特征和结构特征，对灰度图像提取 Hu不变矩特征、灰 度共生矩阵特征、局部二值模式特征和 Gabor 特征，对纹理图像提取分形维数，共得到 2183 维特征参数。再采用主成分分析与线性 评判分析相结合的方法对叶片多特征进行特征降维，将叶片高维特征数据降到低维空间。使用降维后的训练样本特征数据对支持向量 机分类器进行训练(plant species identification method is mainly based on blade automatic identification of low dimensional characteristics.However, can not fully describe blade low-dimensional feature information, identification accuracy is low, in this paper A kind of plant leaves recognition method based on multiple feature dimension reduction.First by digital image processing technology to the plant leaf color sample image preprocessing, obtain background color removal, wormhole, petioles, and the blades of a binary image, gray image and texture image.Then the binary image to extract the geometric characteristics and characteristics of structure and characteristics of gray image extraction Hu moment invariants, gray co-occurrence matrix feature, local binary pattern features and Gabor, to extract the fractal dimension of texture image, get 2183 d characteristic parameters.By principal component analysis and linear uation analysis method of combining the characteristics of blade more feature dimensi)

1.打开左右两张影像2.Harris算法提取同名点3.显示提取同名点（若要进行下一步匹配则不建议显示，这里只是用于查看是否提取出）4.相关系数匹配（只在找出的同名点内匹配）5.LSM最小二乘匹配（先进行粗匹配，再精匹配）(1 Open the left and right images 2.Harris algorithm to extract two tie points 3 shows the extraction of corresponding points (not recommended to proceed to the next match show, here is used to see if extracted) 4. Correlation coefficient matching (just looking match within a point of the same name) 5.LSM least squares matching (rough first match, then fine match))

BMP图像边缘提取后 根据边缘点集直线拟合(BMP image edge extraction fitting a straight line)

傅里叶变换及其逆变换的基于VC6.0实现(Fourier transform and its inverse transform)

M估计子，计算机视觉大牛Peter Meer出品。(Generalized Projection based M-estimator C++ code to find the robust estimate derived without using any user supplied scale. The theory is described in Generalized Projection Based M-stimator. )