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adaboost_haar_bylaoYu
- 利用OpenCv开发的一个基于adaboost算法和haar小波变换的人脸算法,具体情况可参考Paul Viola ,Robust Real-Time Face Detection,2004-OPEN development of the use of an algorithm based on adaboost haar wavelet transform and the Face algorithm specific reference Paul Viola, Robust Real-Tim
icsiboost-0.3.tar
- Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the
RF54_AV_d1_trx
- DM642平台上的H.264源码(可实时运行)-DM642 platform H.264 source (which can be run in real-time)
ResearchOnKeyTechnologiesOfRobustFaceTrackingSyste
- 鲁棒人脸跟踪系统学位论文；提出了一种两阶段的光照均衡的方法来消除单幅图像中的各种阴影包括模糊阴影，投射阴影等 根据快速的Adaboost 训练框架，本文提出了一个实时的鲁棒人脸检测算法 提出了一种基于在线实值boosting 的方法来处理人脸在跟踪过程中发生的外貌变化；实现了一个基于以上模块的完全自动化的人脸跟踪器-Robust Face Tracking System dissertation proposes a two-stage light-balanced approach to t
online
- 实时的训练和分类图像中的画面，通过熟练运用online的性质，运用那个adaboost原理来实现-Training and classification of real-time images of the screen, through the skilled use of the nature of online, using the principle of AdaBoost to achieve
facedetect_harrcascade
- 基于adaboost的构造的cascade用于快速检测人脸，能够实时检测视频中的人脸。没有采用opencv的代码。-Adaboost based on the structure of the cascade for the rapid detection of human faces, real-time video can face. Did not use the code opencv.
ObjectRecognize
- 实时运动物体分割，识别，分类算法，效果不错，采用弱分类器 gentel adaboost 算法-Real-time Object Classification in Video Surveillance Based on Appearance Learning
Robust_Real_time_Object_Detection
- 一个关于real adaboost的paper，里面也介绍了integral image的理论，这是我研究人脸辨识的参考资料。-A paper about real adaboost , which also introduce the theory of integral image. This is my reference of face detection.
PedestrianDetectionMethodBasedonAdaboostAlgorithm.
- Adaboost算法简单可靠、学习精度高的特点，提出一种基于Adaboost算法的行人实时检测方法的论文。-Adaboost algorithm is simple, reliable, high precision, the characteristics of study proposed, based on Adaboost algorithm for real-time detection method of pedestrian papers.
VideoFaceDetect
- 使用matlab调用opencv做成的adaboost人脸检测DLL模块，在usb摄像头采集的视频序列中检测人脸并实时显示， 实时性比C低。 下载后可以直接在matlab运行，不要改动文件夹内的文件相对位置。-Opencv matlab call made using adaboost face detection DLL module, usb camera capture video sequences of face detection and real-time displa
RobustRealtimeObjectDetection
- 人脸实时检测今年来最权威的文章，包括haar小波特征和adaboost的详细原理，violaJones著作。-Robust Real-time Object Detection。
ViolaJones_IJCV
- 基于adaboost的人脸检测，大牛写的，后来写的OPENCV人脸检测都是根据他的这篇论文写的，很有价值-Robust Real-time Object Detection
FindFace
- Adaboost 人脸检测的程序，实现静态图片人脸检测，摄像头实时人脸跟踪功能。速度较快，正脸检测率较高。-Adaboost face detection process, to achieve a static image, face detection, camera, real-time face tracking. Speed, face detection rate is higher.
FACE_PROJECT_opencv
- this adaboost based real time face detection and tracking system i used a adaboost and camshift algorithm with opencv and vc++ The detεction efficiency of the method is not good for environment of dynanlic illumination. We propose a combined meth
Adaboost
- 基于Adaboost算法的多姿态人脸实时视频检测江苏大学-Adaboost algorithm based on multi-pose face detection real-time video
Afacedetect_hd
- 基于adaboost的构造的cascade用于快速检测人人脸，能够实时检测视频中的人脸。没有采用opencv的代码。 -Based on the cascade of adaboost constructed for rapid detection of all face, real-time detection of the face in the video. Of opencv code.
Tadaaboost_hai
- 运用OpenCv开发的一个一种基于adaboost算法与haar小波变换换的人脸算法，具体情况可参考Paul Viola ，Robust Real -A use OpenCv development based adaboost algorithm haar wavelet transform to change the face algorithm, the specific circumstances of the reference, Paul Viola, Robust the Re
adboost
- 实现了三种不同的AdaBoost算法:Real AdaBoost, Gentle AdaBoost and Modest AdaBoost.-Realize the three different AdaBoost algorithm:Real AdaBoost, Gentle AdaBoost and Modest AdaBoost.
RET_iccv13_preprint
- Very recently tracking was approached using classification techniques such as support vector machines. The object to be tracked is discriminated by a classifier from the background. In a similar spirit we propose a novel on-line AdaBoost featur
OpenCVAdaboost__
- 目前支持这种分类器的boosting技术有四种： Discrete Adaboost, Real Adaboost, Gentle Adaboost and Logitboost。-Currently support this classifier boosting technology, there are four: Discrete Adaboost, Real Adaboost, Gentle Adaboost and Logitboost.