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用hough变换提取直线的C++代码，效果比较好，可以灵活改变可提取直线的条数，适用于提取多条直线。(line detecting using hough tranaformation)

vc++6.0进行编程实现对图像的平移处理(vc++6.0 program to achieve the translation of the image processing)

利用opencv+VC++实现对图像的边缘处理，从而对图像进行检测(Use opencv+ VC++ to achieve the edge of the image processing to the image detector)

将某一8位的raw格式图转换为16位的raw格式图 并可输出raw格式图的灰度值至相应文件（.txt文件） 生成的新图用PS打开时与原图基本一致.(It can converted an 8-bit raw format, to an 16-bit raw format and can output raw format diagram of the gray value to the corresponding map file (. Txt files) .The new plan to open with PS are basically consistent with the Original.)

说明：  用opencv编写的视频捕捉以及边缘提取的程序，调试成功，另附调试用的avi视频，希望对大家有所帮助(Opencv prepared with video capture as well as the procedures for edge detection, debugging success, followed by the avi debug video, hope to help everyone)

本程序可以使用图像处理算法对图片进行处理，对于一个含有各种物体的图片，如米粒，可以计算物体的个数。使用前请配置好opencv环境。希望对大家学习图像处理和opencv有所帮助...(This procedure can use the image processing algorithm for processing images for a picture with a variety of objects, such as a grain of rice, can calculate the number of objects. Configured before use opencv environment. We would like to learn opencv image processing and help ...)

用CUDA并行处理方法改写的图像降噪算法，包括KNN,NLM,以及改进的NLM方法。配置环境适合VS2010+CUDA5.0,对使用CUDA的同学来说是很好的教材。(Image denoising algorithms including KNN,NLM and refined NLM run on CUDA, useful demo for who learns CUDA based parallel programming.)

1.打开左右两张影像2.Harris算法提取同名点3.显示提取同名点（若要进行下一步匹配则不建议显示，这里只是用于查看是否提取出）4.相关系数匹配（只在找出的同名点内匹配）5.LSM最小二乘匹配（先进行粗匹配，再精匹配）(1 Open the left and right images 2.Harris algorithm to extract two tie points 3 shows the extraction of corresponding points (not recommended to proceed to the next match show, here is used to see if extracted) 4. Correlation coefficient matching (just looking match within a point of the same name) 5.LSM least squares matching (rough first match, then fine match))

mfc图像处理，实现图像的配准，自己编写，可以应用(mfc image processing, image registration)

opencv SIFT源码详细分析，代码能够准确实现(opencv SIFT detailed analysis of the source code can achieve accurate)