color-card

DOG检测算子，能够构造高斯尺度空间，并检测其中的极值，从而得到有价值的特征点位置(DOG detection operator, the Gaussian scale space can be constructed, and tested one of the extremes, resulting in valuable feature position)

广义高斯概率分布函数的指数估计，包括矩估计和最大似然估计，并对这两种估计进行了比较(exponential cosfficients estimation for generalized Gaussian pdf，include moment estimator and Maximum Likehood estimator. )

图像的读入，放大，cany检测，二值化，灰度化，任意角度旋转(Read image, zooming, cany detection, binarization, gray, and arbitrary rotation angle)

实现图像的滤波、锐化、边缘检测、微分处理及图像平滑(Image filtering, sharpening, edge detection, differential processing and image smoothing)

对测井曲线进行重构的程序，可以充分利用测井特征曲线所反应的岩性特征信息，完成多信息的融合(Log curve reconstruction procedures)

压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为OMP算法，对256*256的lena图处理，比较原图和OMP算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间 (Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix and OMP algorithm as the reconstruction algorithm. Compare lena figure and the reconstruction results using OMP algorithm at different sampling ratio (0.74,0.50.3),then each runs 50 times, compare the performance of PSNR and each running time)

to find the the error in orginal and filtered image

运用harr特征+SRC（稀疏表示）分类实现的一种车辆检测方法，文件中提供了训练和测试车辆图片。由于时间原因，所用haar特征没有优化，维度过高，导致滑窗框图过慢，本代码只输出效果统计数据，以供大家参考学习稀疏表示在车辆检测中的应用。(Using harr feature+SRC (sparse representation) classification to achieve a vehicle detection method, the paper provides a training and test vehicle picture. Due to time reasons, the use of haar feature is not optimized, high dimension, resulting in sliding sash figure is too slow, the effect of the code only output statistics for your reference learning sparse representation in the vehicle detection.)

计算机图形学中小例子，MFC下实现Sutherland_Cohen直线段裁剪算法(Computer graphics small example)

可以进行图象处理,一些简单变化,使用范围比较广泛(it can change image)