用VC++实现透明窗体

SerialH263.zip 串口和H.263-SerialH263.zip serial and H.263

C语言的OPENVG的1.1参考实现，速度慢，可作OPENVG算法实现的参考。-C language reference implementation OPENVG 1.1, slow OPENVG algorithm can be used for reference.

完成卡拉曼滤波,具有一定独特的特点,对初学者很有用-Caraman complete filtering, has a certain unique characteristics, useful for beginners

图像边缘检测算法源代码-Edge detection algorithm source code

用vc编写的数字识别系统，能够识别0到9共十个数字-Vc prepared using digital identification system to identify a total of 0-9 10 figures

用于图像分隔的程序，本算法速度快分隔效果好，无解压密码-separate images for the procedure, the algorithm faster separated effective, extracting passwords

实用.....经本人检测...............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................

图像缩放,首先读入一张图片，调用相应在的函数进行图像缩小或进去放大-Image scaling, the first time into a picture, call the appropriate function in the image to reduce or enlarge into

Graphics_Pipeline_Performance

Abstract We present a component-based, trainable system for detecting frontal and near-frontal views of faces in still gray images. The system consists of a two-level hierarchy of Support Vector Machine (SVM) classifiers. On the first level, component classifiers independently detect components of a face. On the second level, a single classifier checks if the geometrical configuration of the detected components in the image matches a geometrical model of a face. We propose a method for automatically learning components by using 3-D head models. This approach has the advantage that no manual interaction is required for choosing and extracting components. Experiments show that the componentbased system is significantly more robust against rotations in depth than a comparable system trained on whole face patterns.