SPA

演示了点阵字符的绘制方法，visual c++源码(Demonstrates a dot matrix characters drawing method, c++ source code)

实现对图像的经验模态分解，得到经验模态分解后的第一层IMF分量，反映了图像的主要信息，去除冗余信息，为识别提供了基础。(Empirical mode decomposition of image, obtain the empirical mode decomposition of the first layer of IMF component, reflected the main information of the image, to remove redundant information, provide the basis for identification. )

说明：  压缩感知的经典代码，包括观测，稀疏表示，重建，其中观测用的高斯观测矩阵，稀疏用的DCT，重建用的OMP算法(Compressed sensing of the classic code, including observations, sparse representation, reconstruction, observed with a Gaussian observation matrix, sparse use of the DCT, re-use of the OMP algorithm)

VisualC++数字图像处理技术详解第2版光盘，第十章(VisualC++ digital image processing technology Detailed Version 2 discs, Chapter X)

一个全面的图像处理平台，基本上具有教课书上所说的所有图像处理算法，并附带一些关键算法！()

PACS系统的图像浏览软件的分析与设计 硕士学位论文 DICOM通讯(PACS system, image viewing software analysis and design of a master' s degree thesis DICOM communication)

delaunay三角化程序 delaunay三角化程序 delaunay三角化程序(delaunay triangulation delaunay triangulation process procedures delaunay triangulation process)

基于visual c++平台，相机标定方法中的直接线性变换方法，即DLT方法(Based on visual c++ platform, camera calibration method of direct linear transformation method, namely DLT method)

Keystone Transform in ground moving target detection，it s very useful new comer(Keystone Transform in ground moving target detection)

说明：  使用gpu、cpu并行进行sift算子计算匹配，能够在原来的基础上加速处理，但对显卡要求较高，具体环境配置使用方法可以参照mannual(SiftGPU is an implementation of SIFT [1] for GPU. SiftGPU processes pixels parallely to build Gaussian pyramids and detect DoG Keypoints. Based on GPU list generation[3], SiftGPU then uses a GPU/CPU mixed method to efficiently build compact keypoint lists. Finally keypoints are processed parallely to get their orientations and descriptors. SiftGPU is inspired by Andrea Vedaldi s sift++[2] and Sudipta N Sinha et al s GPU-SIFT[4] . Many parameters of sift++ ( for example, number of octaves, number of DOG levels, edge threshold, etc) are also available in SiftGPU. The shader programs are dynamically generated according to the parameters that user specified. SiftGPU also includes a GPU exhaustive/guided sift matcher SiftMatchGPU. It basically multiplies the descriptor matrix on GPU and find closest feature matches on GPU. Both GLSL and CUDA implementations are provided. )