KPCA-demo2

Implementations of image processing algorithms(This project can be built by Visual Studio 2012. Project description file included. Currently it is for Windows only.)

显示BMP图片，并绘制直方图。程序无误，可运行。(Display BMP image, and draw the histogram. Program is correct, you can run.)

利用位图图像中的冗余数据位，将小文件嵌入到大的图像文件中，并保持原图像不变。(The use of bitmap images in the redundant data bit will be embedded in small files to large image files, and to maintain the original image unchanged.)

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.)

视频演示算法包括： 1. 静态背景下的背景预测法目标检测 2. 静态背景下帧间差分法目标检测 3. Mean Shift目标跟踪方法 4. 重心多目标跟踪方法 (Algorithm for video presentation include: 1. Static background background prediction target detection 2. Static background frame difference method for target detection 3. Mean Shift tracking method 4. Gravity multi-target tracking method)

说明：  对图像进行中值滤波，去除噪声，并与原图像进行比较(Median filter on the image to remove noise, compared with the original image)

desing and develop a cube rotation

影像仿射变换特征提取，结合 sift 等局部特征可以处理较大视角变化下的图像匹配。(This is an implementation of Hessian-Affine detector. )

对两幅图像进行配准，分别提取两幅图像的surf特征点以及描述子，得到粗匹配结果，然后根据粗匹配结果，采用ransac方法计算基础矩阵，并去除误匹配点，得到较准确匹配结果(Two image registration, surf was extracted from the feature points in two images to get the coarse matching and descriptor, then according to the results, the coarse matching results, using RANSAC method to calculate the fundamental matrix, and eliminate the error matching, gain a more accurate matching results)

这是一段视频分割的源代码 效果比较好 希望对大家有帮助(This is a video segmentation of the source code for the effect we hope to help)