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实现图像的几何变换，包括图像的平移、镜像变换、缩放、转置以及旋转。(Achieve geometric transformation of image, including the image translation, image transformation, scaling, transpose and rotation.)

针对大尺度图像配准和不同传感器图像配准问题, 介绍了一种基于 SIFT 的图像配准方 法。(For large-scale image registration and image registration problem from different sensors, introduces a SIFT-based image registration method.)

实现原始的JPEG图片编解码，不借助任何的第三方库，纯C++。(To achieve the original JPEG image decoding, without using any third-party libraries, pure C++.)

photoshop的各个效果的详细介绍，方便研究photoshop的各种算法，值得一看啊！！(photoshop detailed description of the various effects, photoshop various algorithms to facilitate research, worth ah! !)

图像处理标准测试图片库，用于数字图像处理各种仿真实验。(Standard test image gallery image processing to digital image processing all kinds of simulation experiments.)

利用直方图双峰法选取适当阈值对图像进行二值化处理。此法只适用于直方图为双峰状图像。(Using the Shuangfeng method to the appropriate threshold histogram of image processing two values. This method is only applicable to the histogram of image of Shuangfeng.)

改进的聚类光流法，聚类方法有kmeans和DBSCAN，光流法金字塔LK可以用，HS光流法占用内存太多。(Improved clustering optical flow method, clustering method kmeans and DBSCAN, pyramid LK optical flow method can be used, HS optical flow occupy too much memory.)

图形图像处理算法大全，包含各种算法的源码，只要经过简单修改就可变为程序(Daquan graphic image processing algorithms, including the source code for various algorithms, as long as you can into a program after a simple modification )

YUV Quantization using VS2010

基于遗传模拟退火和Otsu法的图像分割。采用一种基于模拟退火算法(SA)的混合遗传算法(GASA),来进行Otsu方法选择阈值，其中进行求解时是非线性的遗传算法（GA）的应用优化了求阈值过程，并尽可能地减少其运算量,而加入SA则避免了遗传算法(GA)常见的早熟收敛现象,从而实现有效简单的图像分割。 (adopt hybrid genetic algorithm(GASA) based on simulated annealing algorithm,choose threshold by Ostu method,which applicated the non-linear genetic algorithm(GA) allows us to optimize the process of threshold. And, where possible, to reduce the computation. in order to achieve effectivly and simply image segmentation. Combination SA is to avoid the common phenomenon about premature convergence in genetic algorithm (GA) to achieve a simple and effective image segmentation.)