YUV_DCT

说明：  编程实现对bmp图像最大值，最小值，标准差，熵，各灰度值频率的计算(Programming to calculate the maximum value, minimum value, standard deviation, entropy and gray value frequency of BMP image)

说明：  在VC中实现了使用Moravec算子对遥感影像进行特征点的提取，缺点是速度较慢。不过提取效果不错！(Implemented in VC using Moravec operator of remote sensing images feature point extraction, the disadvantage is slow. However, extracting good results!)

此程序利用openCV中人脸识别于模版匹配实现机器人的运动控制功能，其中机器人控制用了Aria的库和模拟器，使用者可根据控制需要替换这部分程序。 本程序的核心部分一直用于只能轮椅的人脸控制。(This process us face recognition in openCV and template matching to achieve robot motion control functions, which used the Aria library and robot simulator, user need replace this part of the control procedures to achieve their application.)

基于图像对比度和波段相关性的波段选择算法，适用于高光谱图像的特征选择(Bands selection algorithm based on image contrast and correlation of bands for hyperspectral image feature selection)

《VC++数字图像识别技术典型案例》第九章光学字符识别技术，包括预处理、边缘跟踪和特征提取算法("VC Digital Image Recognition Technology typical case," the ninth chapter of optical character recognition technologies, including pretreatment, Edge tracking and feature extraction algorithm)

一个vc编的图像处理的程序，处理不同颜色图像(a series of image processing procedures for handling images in different colors)

利用opencv库写的一个关于图像孔洞填补的小程序，附加了效果图片(Using the opencv library to write a small program on the filling of the hole, the additional effect of the picture)

针对空间发光点缩成光斑图像中心的高精度提取问题，本文提出一种亚像素提取方法。该方法简历了具有高斯能量分布的空间发光点的透视投影光斑图像灰度分部数学模型，证明了空间发光点能量中心的透视投影不变性。(Light-emitting points of space shrunk to spot high-precision center of the image extraction problem, we propose a subpixel extraction method. This method resumes a space with Gaussian energy distribution points of perspective projection spot light gray segment mathematical model to prove the energy center of the space light-emitting point perspective projection invariance.)

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. )

直线段检测算法EDLines C++实现(EDLines C++ Opencv lins segment detection)