Cohen-SutherlandLiang-Barsky

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)

数字图像处理中图像的增强方便的源代码，包括图像灰度修正，图像的平滑，中值滤波，图像的锐化，伪彩色和假彩色的增强等(Digital image processing image enhancement convenient source code, including the gray correction, image smoothing, median filtering, image sharpening, pseudo-color and false color enhancement, etc.)

图像的分割处理，可以将图像中的数字分割找出(IMAGE division)

图像识别的小软件，识别率还算凑合吧，大家共同学习(Small image recognition software, the recognition rate still make it, we learn together )

说明：  可以实现旋转，错切，各种投影图，透视图。是学习三维变换很好的原始参考资料(Can rotate, shear, a variety of projection, perspective. Is to learn the original three-dimensional transform good reference)

说明：  LSA的第一步是要去创建词到标题（文档）的矩阵。在这个矩阵里，每一个索引词占据了一行，每一个标题占据一列。每一个单元（cell）包含了这个词出现在那个标题中的次数。例如，词”book”出现在T3中一次，出现在T4中一次，而”investing”在所有标题中都出现了一次。一般来说，在LSA中的矩阵会非常大而且会非常稀疏（大部分的单元都是0）。这是因为每个标题或者文档一般只包含所有词汇的一小部分。更复杂的LSA算法会利用这种稀疏性去改善空间和时间复杂度。(The Little Book of Common SenseInvesting: The Only Way to Guarantee Your Fair Share of StockMarket Returns ,)

内置虑镜功能，包括获取图像的属性，锐化，模糊，浮雕等功能． 可以缩放，优化图片．打开图片带预览功能,使用了gdiplus,能处理gif,bmp,jpg,png图片,处理速度也不错，呵呵,一直找图形格式转换的朋友就不需要东找西找了，它完全可以实现你的需求！ (Built-in lens into account features, including access to the properties of the image, sharpen, blur, emboss and other functions. Can be scaled to optimize picture. Open the image with the preview function, use the gdiplus, can handle gif, bmp, jpg, png images, the processing speed is not bad, huh, huh, graphic format conversion has been looking for friends do not need to look east and west, and it can achieve your needs!)

Some of image processing tools in visual C++

用C++编写的产生文章的程序，方便有需要的人(C generated articles prepared by the procedures that are accessible to people in need)

说明：  很好用的三维重建软件，VisualSFM软件允许上传一系列图像，它从这些图像中找到每一个图像的特定特征，利用这些特征信息重建出3D模型的稀疏点云，而后还可进行稠密点云重建。(The VisualSFM software, a good 3D reconstruction software, allows uploading a series of images. It finds the specific features of each image from these images, and USES these feature information to reconstruct the sparse point cloud of the 3D model, and then it can reconstruct the dense point cloud.)