PicViewerDemo

说明：  该书介绍的用VC实现图像处理，其中包括一些图像处理知识的介绍(The book described the realization of image processing using VC, including some knowledge of image processing introduction)

说明：  图象压缩方面的论文 可用于毕业设计方面的工作(Image compression can be used for graduate thesis work of design)

在GPU-CUDA平台上进行SIFT特征提取，加快提取速度(On GPU-CUDA platform SIFT feature extraction, speeding up the extraction rate)

基于颜色来检测火焰，将是频频分帧，然后逐帧检测，碧昂切将火焰全画出来，基础编程。(Based on color to test the flame, basic programming )

光斑中心提取算法，在VS2010下，用opencv2.3.1编写的控制台程序，主要功能是利用hession矩阵和高斯滤波进行光斑中心点提取，里面附测试图片，提取精度为亚像素级别。(Spot center extraction algorithm, in VS2010, written by opencv2.3.1 console program, the main function is to use hession matrices and Gaussian filter for extracting the center spot, which is attached test images, the extraction accuracy of sub-pixel level.)

毕业设计，基于内容的图像检索，支持的检索特征包括 sift,颜色直方图，灰度矩阵，HU不变矩，边缘方向直方图，检索方法使用K-means和K-D树两种，需要OPENCV支持，运行时请先选定一个文件夹来生成特征库，特征库用access数据库保存，只支持JPG文件(Graduate design, content-based image retrieval, search features, including support sift, color histogram, gray matrix, HU moment invariants, edge direction histogram, retrieval method using the K-means and KD trees are two kinds of needs OPENCV support Please select a runtime folder to generate the feature library, feature library with access database save, only supports JPG files)

该源码为目标检测和跟踪算法 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 (The source code for target detection and tracking algorithm include: 1 Static background background prediction target detection 2 Static background frame difference method for target detection 3. Mean Shift tracking method 4 multi-target tracking methods focus)

傅里叶变换及其逆变换的基于VC6.0实现(Fourier transform and its inverse transform)

运用于人脸图像预处理中，主要实现功能有：人脸检测，人脸对齐，人脸图像放缩，人脸剪切。(Applied to the face image preprocessing, the main features are: face detection, face alignment, face image zoom, face cuts.)

基于visual c++平台，相机标定方法中的直接线性变换方法，即DLT方法(Based on visual c++ platform, camera calibration method of direct linear transformation method, namely DLT method)