opencv毛星云著， 和适合初学者练手

说明：  C# SDK for Face++ 人脸检测与分析 检测给定图片(Image)中的所有人脸(Face)的位置和相应的面部属性 检测模式可以是normal(默认) 或者 oneface 。在oneface模式中，检测器仅找出图片中最大的一张脸。 可以是none或者由逗号分割的属性列表。默认为gender, age, race, smiling。目前支持的属性包括：gender, age, race, smiling, 可以为图片中检测出的每一张Face指定一个不包含非法字符且不超过255字节的字符串作为tag，tag信息可以通过 /info/get_face 查询 检测给定人脸(Face)相应的面部轮廓，五官等关键点的位置，包括25点和83点两种模式。(Face Detection and Analysis Detecting the location and corresponding facial attributes of all faces in a given image The detection mode can be normal (default) or oneface. In oneface mode, the detector finds only the largest face in the picture. It can be none or a comma-separated list of attributes. The default is gender, age, race, smiling. Attributes currently supported include gender, age, race, smiling. Each Face detected in a picture can be tagged with a string that does not contain illegal characters and does not exceed 255 bytes. The tag information can be queried by / Info / get_face. The location of key points such as facial contour and facial features for a given face (Face) is detected, including 25 points and 83 points.)

在vs2010平台下，使用opencv2.3库写的人脸检测程序，使用opencv自带的haarcascade_frontalface_alt.xml，亲测可以使用，适合刚刚接触opencv和人脸检测技术的朋友，欢迎下载。

基于OPENCV实现的粒子滤波运动目标跟踪源程序(Particle Filter Moving Object Tracking based on OPENCV)

说明：  通过opencv与visualc++加载一副图像，在c++中读取图像(By loading an image opencv with visualc++, to read images in c++)

opencv实现canny边缘检测。。。。。。。(opencv realize canny edge detection)

基于hog算法的人数识别程序，使用了openCV视觉库，效率高，主要是边缘检测(Algorithm based on the number of hog identification procedures)

基于opencv的车辆数量计数器,Visual C++,完整的程序代码(a simple_vehicle_counting-master based on Opencv)

Object detection (or rather, recognition) is one of the fundamental problems in computer vision and a lot of techniques have come up to solve it. This is a Project about Real-time object detection in OpenCV using SURF.

使用OpenCV实现车辆的检测与跟踪的源代码，实现道路车辆的检测与跟踪，使用用质心跟踪法，visual c++实现

OpenCV概述，OpenCV入手，模块介绍，与Intel IPP交互，Python界面及应用实例(General information, getting started with OpenCV, modules description, interaction with Intel IPP, Python interface, some usage examples, summary)