Dual_DC_DC

使用Visual C++编写的生成三次Hermite曲线的源代码。(Bicubic Hermite code in Visual C++.)

利用位图图像中的冗余数据位，将小文件嵌入到大的图像文件中，并保持原图像不变。(The use of bitmap images in the redundant data bit will be embedded in small files to large image files, and to maintain the original image unchanged.)

用visual c++编写的飞机飞行模拟航迹，其中包括VC源代码和模拟飞行图。(Written with visual c++ simulation of aircraft flight tracks, including the VC source code and simulation of flight plans.)

说明：  在autocad2007环境中直接使用API就可以亮显实体的一个示例演示(This program demonstrates how to use ObjectARX API functions to highlight and unhighlight subentities of complex entities such as ACIS solids.)

这是一个应用VC++编写的基于摄像头读取人脸的人脸识别和人脸检测软件，其中引用了MATLAB的系统函数，是一个非常不错的人脸识别软件，这个系统在我校论文答辩审核中获得了优秀的成绩。希望对大家有用(This is an application-based VC prepared to read camera Face to Face Recognition and face detection software. cited in the MATLAB system function, is a very good face recognition software. The system in my school examination papers reply obtained outstanding achievements. We hope to useful)

基于格子玻尔兹曼方法模拟泊肃叶流，边界条件用非平衡格式(Poiseuille flow based on the lattice Boltzmann method to simulate the boundary conditions with unbalanced format )

高光谱图像的地物分类技术，对光谱分析并对比检测出地景的算法(Fifth hyperspectral image classification techniques, algorithms for spectral analysis and contrast detect landscapes)

DIX公式优化求解层速度，使得速度变得更加平滑。(DIX formula optimization solution velocity, making speed becomes smoother.)

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：  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. )