guassRandA

Criminisi方法修复灰度图像（单通道）图像(Criminisi algorithm for gray image or single channel image )

对所采样的原始图像进行噪声滤波、边缘检测、特征提取、利用多项差值亚像素算法。(the target image is be processed that it is filtered noise, edge detection and character be got.)

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)

说明：  在产生混沌伪随机序列的混沌映射中，Logistic映射(Logistic Map)是一种简单而经典的映射。其中u称为分形参数，随着u逐渐增大，出现倍周期分岔现象。可用于密文域图像加密。(Logistic Map is a simple and classical mapping for generating chaotic pseudo-random sequences. U is called fractal parameter, and with the increase of u, period doubling bifurcation occurs. It can be used for image encryption in ciphertext domain.)

通过对DXF文件解析，将解析后的数据以列表的形式展示在winform窗体中

说明：  图形处理功能，有滤波功能，边缘提取等各个方面，值得一看(Graphics processing capabilities, has filtering, edge detection and other aspects, see)

本代码实现的功能：24位位图（BMP）的灰度化。具体实现过程：打开图像文件,找到数据区(每3个字节代表一个象素的R,G,B值),循环扫描,根据RGB的权重,计算出灰度值. 如果要得到256色灰度图,则要新建一个调色板,修改信息头和文件头.(Realize the function of the code: 24-bit bitmap (BMP) of the gray. Concrete realization of the process: Open the image file, locate the data area (3 bytes per pixel on behalf of a R, G, B values), cyclic scan, according to the weight of RGB, gray value is calculated. If you want to get 256 colors grayscale, would create a new palette, modify the header and file header information.)

基于模板匹配的另一种人脸识别的算法，很实用，利用肤色建模来建立人脸皮肤模型(Another based on template matching algorithm for face recognition, very practical, using skin color modeling to create a human face skin model)

稀疏自适应匹配追踪算法，无需稀疏度，就可以重构原始信号。(Sparse adaptive matching pursuit algorithm, without sparsity, we can reconstruct the original signal.)

斯坦福大学的姚邦鹏开发的一个图像分类算法，使用random forest实现了图像的精细区域描述，赢得了PASCAL VOC2011图像分类竞赛中的winner prize.经测试，程序完全可运行，且提供了MAC和Windows下的两种程序。(Image Classification: An Integration of Randomization and Discrimination in A Dense Feature Representation The goal of our method is to identify the discriminative fine-grained image region that distinguishes different classes. To achieve this goal we sample image regions from dense sampling space and use a random forest algorithm with discriminative classifier. Each node of the tree of random forest is trained and tested with fine-grained image patches combining the information from upstream nodes together. We implemented each node of the tree with a discriminative SVM classifier, which makes the node as a strong classifier. )