DIBDisplay

Opencv处理图像，用ostu法实现图像的二值化(Opencv image processing, using ostu method to achieve image binarization)

利用visualC++和图像处理的知识，编写程序对果蔬进行自动识别(Using visualC++ and image processing knowledge, write a program for automatic identification of fruits and vegetables)

特征点匹配时，采用构造KD-tree，用最优节点优先法寻找最佳匹配点(Matching feature points, the tectonic KD-tree, with the optimal node is preferred method to search for the optimal matching point)

C++和halcon结合，用于图形处理时进行三相机标定(C++ and halcon combined for three camera calibration)

最小二乘匹配，图像匹配算法，需要初始匹配坐标，可以达到亚像素精度(least squares matching, image matching algorithm, the initial need to match coordinates, can reach sub-pixel accuracy)

一种基于背景减法和帧差的运动目标检测算法(Based on background subtraction and frame difference moving target detection algorithm)

对图像变形的一些基本算法的介绍和功能的描述等 (about marphing )

说明：  利用大津算法对图片进行处理，得到满意的处理效果。(The image is processed by Otsu algorithm and satisfactory results are obtained.)

程序将界面与数据处理分开，界面采用MFC的对话框模板，对注册、训练、识别和主界面分别建立一个类，通过消息处理函数与用户进行交互，利用多线程来实时显示图像。 数据处理又分为了CFaceAlign（人脸检测+几何归一化）、CLightPrep（光照归一化）、CFaceFeature（Gabor特征提取）、CSubspace（计算Fisherface子空间）四个类，还有一个类 CFaceMngr 负责管理界面与数据之间的交流。这样的程序结构使对界面和算法彼此独立，互不影响。 每个类的具体方法请见相应头文件中的注释。(The program will interface with the data processing separate interface using MFC dialog template, registration, training, recognition and the main interface, respectively, to create a class that interacts with the user through a message handler, using multiple threads to display images in real time.   Data processing is divided into a CFaceAlign (Face Detection+ geometric normalization), CLightPrep (illumination normalization), CFaceFeature (Gabor feature extraction), CSubspace (computing Fisherface subspace) four categories, as well as a class is responsible for managing CFaceMngr interface and data exchange between. Such a program structure allows for interfaces and algorithms are independent of each other. Specific methods of each class, see the comments in the appropriate header files.)

对输入视频进行处理，使用高斯背景建模的方法，得到视频前景，对于人群密度估计具有很大作用。(Processing of the input video, using a Gaussian background modeling method, to get videos prospects for population density estimation has a significant role.)