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- 利用opencv识别手写数字的分类，并识别，利用了mnist数据库-Using opencv recognize handwritten digits classification and identification, the use of mnist database
cylinder_calibration
- vs2008+opencv环境下，圆柱形棋盘的自标定程序，不使用库函数，自定义算法。可自动判别不同角度拍摄的圆柱棋盘照片上的可用网格数量，进而成功实现标定。-Vs2008+ opencv environment, cylindrical board since the calibration procedure, do not use the library function, the custom algorithm. Can automatically recognize cylindric
face_detection_and_recognition
- 該計劃將教如何使用OpenCV的檢測和識別人臉。-This program will teach on how to detect and recognize face using OpenCV.
markReco
- recognize red and blue dot based on opencv. useful both on image and video.
faceEye
- 基于haar级联分类器的opencv人眼识别程序，思路是先识别人脸，然后以人脸为ROI识别人眼。-Haar cascade classifier based on opencv human eye recognition program, the idea is to recognize faces, and then face the human eye to identify the ROI.
Nhandangkhuonmat
- this program about face recognize, dowload then unzip to use, this program required opencv-this is program about face recognize, dowload then unzip to use, this program required opencv
VIDEOREAD
- opencv视频读取代码，VS2010+CV2.4.9，并自动框取视频中激光区域。-autonomous recognize laser line. based on VS2010+CV2.4.9
365timelapse-master
- The following code will process images taken every day by a user, recognize the location of the face using OpenCV and crop for it. The user can then create a video timelapse of the images.
FaceRecognition-tensorflow-master
- 可以通过摄像头识别是不是本人，主要使用的是opencv进行人脸识别，测试环境是tensorflow(You can recognize me through the camera)
easypr
- 中文车牌识别系统： 它基于openCV这个开源库。这意味着你可以获取全部源代码，并且移植到opencv支持的所有平台。 它能够识别中文。例如车牌为苏EUK722的图片，它可以准确地输出std:string类型的"苏EUK722"的结果。 它的识别率较高。图片清晰情况下，车牌检测与字符识别可以达到80%以上的精度。(Chinese car license plate recognition system: It is based on ope
opencv_traincascade人脸训练无代码
- 人脸检测，采用OPENCV实现，包括训练和检测(face detect, make up by OPENCV, include face detect and face recognize)
lbpcascade_animeface-master
- 利用opencv资源库里自带的lbpcascade_animeface.xml，对普通人脸进行识别，如果有数据库的话，也可以自己训练学习，提取人脸特征，进行学习(We use the lbpcascade_animeface.xml in opencv repository to recognize normal faces. If there are databases, we can also train ourselves to learn, extract facial features