NCC

说明：  PCL+ MFC+ Visualization 点云显示(PCL+ MFC+ Visualization)

此程序利用openCV中人脸识别于模版匹配实现机器人的运动控制功能，其中机器人控制用了Aria的库和模拟器，使用者可根据控制需要替换这部分程序。 本程序的核心部分一直用于只能轮椅的人脸控制。(This process us face recognition in openCV and template matching to achieve robot motion control functions, which used the Aria library and robot simulator, user need replace this part of the control procedures to achieve their application.)

灰度数字图像增强处理,灰度数字图像增强处理,灰度数字图像增强处理,(Deal with gray-scale digital image enhancement)

这是三维点云数据边缘线提取的c++算法，适合于LIDAR数据的三维处理，供大家参考学习(This is 3 d point cloud data edge extraction of c++ algorithm, suitable for LIDAR data 3 d processing, provide everyone reference to study)

基于形状的模板匹配，用到了opencv，主体包含两部分，建模与金字塔搜索。(Based on the shape of the template matching, the use of opencv, the main body contains two parts, modeling and pyramid search.)

该程序主要实现图像的无损压缩，具体算法包括： （1）整数小波变换＋SPIHT无损压缩 其中整数小波可以采用JPEG2000提供的5－3小波，也可以采用S＋P变换，本程序均已给出。 （2）DPCM＋算术编码 DPCM采用的是JPEG中采用的方法； （3）JPEG－LS标准 JPEG－LS的无损压缩性能非常好，超过JPEG2000。(The program mainly realize lossless image compression, the specific algorithm include: (1) integer wavelet transform SPIHT Integer Wavelet lossless compression which can be used to provide the 5-3 wavelet JPEG2000, can also adopt the S P transform, the procedures are given. (2) DPCM arithmetic coding DPCM is used in JPEG methodology (3) JPEG-LS standard JPEG-LS lossless compression of very good performance, more than JPEG2000.)

说明：  利用c++实现的PCA算法，直接对数据进行处理，适合初步学习(C++ implementation using PCA algorithm to process the data directly)

换热器模型的MATLAB实现,可用于工程计算,还可以用于教学演示(Heat exchanger model of MATLAB and can be used for engineering calculation, and can also be used for teaching demonstration)

检测视频中的运动目标以及阴影，并消除阴影，基于opencv，将RGB颜色空间根据一定的转换关系转换到HSV空间，最后消除阴影， 可以在vc6.0和vs2008上正常运行(Shadow detection algorithm based on HSV color space)

采用了Harrtraining和离子滤波算法实现了拥挤人群人头计数，非常有用。VC++和Opencv开发，采用Oepncv中的Harrtraining训练一个分类器即可运行(Human detection and tracking in high density crowds is an unsolved problem. Standard preprocessing techniques such as background modeling fail when most of the scene is in motion. We integrate human detection and tracking into a single framework, and in- troduce a conrmation-by-classication method to estimate condence in a tracked trajectory, track humans through occlusions, and eliminate false positive detections.)