ImageCutofRGB565BMP

VC摄像图捕捉程序，简单实用，对初学者有帮助(VC camera Figure capture, simple and practical for beginners)

说明：  基于边缘特征的SAR图像与光学图像匹配，测度为Hausdorff距离(SAR images based on edge features and optical image matching, for the Hausdorff distance measure )

计算机图形学基础算法z-buffer， 实现隐藏面消除(Computer graphics based algorithm z-buffer, to achieve hidden surface removal)

利用Artoolkit实现了虚拟现实的算法，VS2008下测试通过(Use Artoolkit algorithm to achieve a virtual reality, VS2008 under test)

Compression of face images impact the performance of face recognition (FR) systems. JPEG Region of Interest (JROI) compression maintains high image quality in facial regions while compressing the background more, with minimal impact on FR performance.(Compression of face images impact the perf ormance of face recognition (FR) systems. JPEG Region of Interest (JROI) compression maintai ns high image quality in facial regions while co mpressing the background more, with minimal impact on FR performance.)

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.)

二值图像连通域分析，和MATLAB中bwlabel函数功能相似(Connected domain analysis of binary image, and bwlabel similar function in MATLAB)

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.)

对圆形，正方形，长方形等几何图形进行计算面积，周长等运算(Circular, square, rectangular and other geometric shapes are calculated area, perimeter of operation)

说明：  LSA的第一步是要去创建词到标题（文档）的矩阵。在这个矩阵里，每一个索引词占据了一行，每一个标题占据一列。每一个单元（cell）包含了这个词出现在那个标题中的次数。例如，词”book”出现在T3中一次，出现在T4中一次，而”investing”在所有标题中都出现了一次。一般来说，在LSA中的矩阵会非常大而且会非常稀疏（大部分的单元都是0）。这是因为每个标题或者文档一般只包含所有词汇的一小部分。更复杂的LSA算法会利用这种稀疏性去改善空间和时间复杂度。(The Little Book of Common SenseInvesting: The Only Way to Guarantee Your Fair Share of StockMarket Returns ,)