Cpp1

视频编解码技术，在有限带宽的情况下，能够大幅度的压缩编码比特数，图像的质量没有丝毫的下降。(Video codec technology, in the case of limited bandwidth can be substantial coding bits, the image quality is not the slightest decline.)

基于SUFT的特征提取与匹配，内涵详细的文档说明，很好的实现了特征提取与匹配，该方法较基本的SIFT有很大的提高(Extraction and matching of features based on SUFT, detailed content of the documentation, very good realization of the feature extraction and matching, SIFT the method is basic is greatly improved)

像素级融合实现 pca his，brovery加权融合方法，很不错(Pixel level fusion to achieve pca his, brovery weighted fusion method, very good)

利用数字图像处理软件halocn来进行缺陷检测，附带有之前做工程项目的原图源代码，还有ppt和实验指导书(Halocn using digital image processing software for defect detection, comes with artwork done before the project source code, as well as ppt and experimental instructions)

数字图像处理用到的标准测试图像，很多图片。(IMP test images。)

利用Mean-shift进行平滑处理，主要是对图像进行平滑的界面，MFC和OpenCV进行混合编程……(Mean-shift smoothing, image smooth interface, the MFC and OpenCV mixed programming ...)

说明：  行人检测HOG+SVM进行图片中行人检测，提供训练用的pos和neg样本，效果还可以；没有SVM工具箱的，压缩包里已经提供了，安装一下即可(Pedestrian detection HOG + SVM for pedestrian detection in pictures, providing POS and neg samples for training, the effect is good; without SVM toolbox, the compression package has been provided, just install it.)

26个不同影像处理程序,以VC++开发,是了解影像入门好帮手(26 different image processing procedures to VC++ Development is helpful to understand the image entry)

二值图像处理运算是从数学形态学下的集合论方法发展起来的,尽管它的基本运算很简单，但是却可以产生复杂的效果。常用的二值图像处理操作有许多方法，如腐蚀、膨胀、细化、开运算和闭运算等等。本文对这些内容作些研究探讨， 希望对爱好图像处理的朋友有所帮助。(Binary image processing algorithms are developed set theory in mathematical morphology. Although its basic operation is simple, it can produce complex effects. Commonly used binary image processing operations there are many ways, such as corrosion, expansion, refinement, open operation and closed operation and so on. In this paper, some of these studies to explore, I hope to love image processing friends help.)

用AAM算法进行人脸特征点的标定，即对人脸的眼睛，眉毛，嘴巴，鼻子及脸的轮廓进行标记，此代码标记了68个特征点。(AAM algorithm for facial feature points calibration, calibration of this code is the 68 feature points, see the instance document.)