Image-magnification-narrow

vc++图像开发的附书光盘,包括众多的实例程序,每个程序都可以是功能完备的工具软件.(visual c++ image develop source code and instances)

VisualC++数字图像处理技术详解第2版光盘-第六章(VisualC++ digital image processing technology Detailed Version 2 CD- Chapter VI)

opencv是图像处理中常用到的工具箱，本资料为基于此工具箱的图像处理匹配算法SURF的实现代码。(opencv is commonly used in image processing to the toolbox, the toolbox based on this information for the image processing algorithm to match the achievement of SURF code.)

说明：  opencv入门程序，使用opencv模式读取图像并显示，在vs环境下调用matlab进行图像读取和显示(opencv entry procedures, read the image using opencv mode and display, in the vs environment, call matlab to read and display the image)

LTE中速率匹配的代码，包括子块交织，比特搜集，比特选择和修剪(The code rate matching in LTE, including the sub-block interleaving and bit gather, bit selection and pruning)

实现鱼眼图 圆形视场和全视场的校正、分为2幅图像、4幅图像、任意角度、任意位置的校正、全景图拼接等(Fish eye diagram of a circular field of view and full field calibration is divided into two images, four images at any angle, any position of the correction, and panoramic stitching.)

说明：  最二小乘影像匹配，精度可以达到0.01个像素，也可以最二小乘影像模板匹配。经过修改后可以直接用，只要调用一个函数就搞定，函数参数的说明很清楚，绝对不会有任何困难，非常好用。(Most two Theravada image matching accuracy can reach 0.01 pixel images can also be the most two Theravada template matching. After modifications can be directly used, as long as is you call a function, function parameters, the description is clear and will not have any difficulties, very easy to use.)

通过点云数据的格网化，建立格网分块索引，这是一种有效的点云数据存储方式。( Through the point cloud data grid , the establishment of a grid block index, which is a valid point cloud data storage. )

一个用MFC实现的图象缩放程序,也有测试图片!(MFC with a picture zoom procedures, tests have pictures!)

一种简单高效地基于压缩感知的跟踪算法。首先利用符合压缩感知RIP条件的随机感知矩对多尺度图像特征进行降维，然后在降维后的特征上采用简单的朴素贝叶斯分类器进行分类。该跟踪算法非常简单，但是实验结果很鲁棒，速度大概能到达40帧/秒(A simple and efficient tracking algorithm based on compressed sensing. Firstly, with the random sensing matrix compressed sensing RIP conditions for multi-scale image feature dimension reduction, and then use the naive Bias classifier simple classification in the feature reduction after the. The tracking algorithm is very simple, but the results are robust, speed can reach 40 frames per second)