sift_ImageProcessing

运用openCV编写的C++图像色彩转移程序，原理是先转化到Lab图像，再保持L不变调整图像的a、b分量的均值跟方差。(Using openCV prepared C++ image color transfer process, the principle is first converted to Lab image, and then adjust the image to keep L constant a, b, with the mean variance component.)

基于OpenCV实现的图像剪切，截取小软件，对学习OpenCV的朋友比较有帮助(OpenCV based on the realization of the image shear, the interception of a small software, to learning more OpenCV friends help)

在visual c++中用分形完成的julia集,是分形的具体应用,对学分形的朋友应该有帮助(In the visual c++ Used to complete the julia fractal set, is the concrete application of the fractal, Friend of the credits should be shaped to help)

VC++图像平滑处理+程序源码，实现图象的平滑（去噪声），锐化功能，测试时请在C盘下放一个测试用的Bmp图片，路径：C: est.bmp，没有的话没法测试。如上图所示是水平平滑处理，可以对比下，不过感觉颜色失真了，有兴趣的看一下。命令行编译过程如下： 　　vcvars32 　　rc bmp.rc 　　cl smooth.c bmp.res user32.lib gdi32.lib(VC++ image smoothing+ program source code, to achieve a smooth image (to noise), sharpening function in the C drive when testing a test of decentralization Bmp picture, path: C: test.bmp, no words can not test . Is shown above the level of smoothing, you can compare the next, but the feeling of color distortion, interested look. Command-line compiler as follows: vcvars32 rc bmp.rc cl smooth.c bmp.res user32.lib gdi32.lib)

完成流程编码和huffman编码，适合做图像编码的熵编码，只要调整接口就好。(Complete the process encoding and huffman coding, image coding suitable for entropy coding, as long as the adjustment of the interface is like.)

边缘检测，并分割，以不同颜色标示。站内有其matlab实现，这是对应的c++代码。(Edge detection and segmentation, marked with different colors. Inside the station are the matlab realize, This is corresponding c++ code.)

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！()

介绍基于彩色不变理论的SIFT算法（CSIFT算法）的文章，文章是英文的(Color change based on the theory introduced SIFT algorithm (CSIFT algorithm) article, the article is in English)

双目立体匹配，使用SAD，SSD，NCC算法，比较多种常见算法的优劣，对其优化实现快速准确匹配。(Binocular stereo matching, the use of SAD, SSD, NCC algorithm, compare the merits of a variety of common algorithms, and its optimization to achieve fast and accurate matching)

该算法可以应用于计算机视觉的物体识别以及3D重构中。(The algorithm can be applied to object recognition in computer vision and 3D Reconstruction.)