搜索资源列表
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基于ADABOOST的人脸检测代码，使用继承的策略，包括特征的计算，提取，ADABOOST训练，级联，最后训练出数据，还有注释，可以帮助大家研究ADBOOST-Based on ADABOOST face detection code, the use of inheritance strategies, including the calculation of features, extraction, ADABOOST training cascade, the last train out
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基于adaboost的人脸检测算法,包括训练，修改了数据读取结构，降低了内存需求-Based on adaboost face detection algorithms, including training, to modify the data read structure, reducing the memory requirements
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opencv 人脸 训练的 一些算法说明 和准备工作-opencv face training preparation instructions and some algorithms
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base on Adaboost handwriting number recognition
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介绍当前所有人脸技术的改进工作.包手肤色建模,Adaboost训练,Suan算法等改进算法.-Introduced all the current technology to improve the working face. Packet hand color model, Adaboost training, Suan algorithm to improve the algorithm.
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adaboost的实现算法，可进行人脸的样本训练-implementation of the AdaBoost algorithm, can be a sample of people face training
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Adaboost训练分类器的源代码，是快速人脸检测的必备。-It is the code for training classifiers by
Adaboost to Face Detection
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基于支持向量机的人脸检测训练集增强算法实现。根据支持向量机(support vector machine，简称SVM)~ ，对基于边界的分类算"~(geometric approach)~
言，类别边界附近的样本通常比其他样本包含有更多的分类信息．基于这一基本思路，以人脸检测问题为例．探讨了
对给定训练样本集进行边界增强的问题，并为此而提出了一种基于支持向量机和改进的非线性精简集算法
IRS(improved reduced set)的训练集边界样本增强算法，用以扩大-91l练集并改
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一篇在人脸检测时用于训练反面训练集的文章，可以-A face detection in the negative training set for training the article, you can see
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人脸检测根据姿态评估，然后利用adaboost方法训练分类器，是一片值得收藏的文章-Face detection based on posture assessment and classification using adaboost training method, is a worthy collection of articles
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针对LBP的人脸检测算法的文章3篇，区别于adaboost的人脸检测算法，opencv有相应的训练数据库。-For three of the articles of the LBP face detection algorithm, the face detection algorithm is different from adaboost, opencv training database.
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关于adaboost算法的人脸检测程序，可以选择不同的训练库进行检测，对adaboost算法的理解有好处。-AdaBoost algorithm of face detection program, you can choose different training library for testing, good understanding of the AdaBoost algorithm.
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一个基于adaboost的人脸检测分类器训练程序，用MMATLAB写的，希望能和大家分享，已通过测试。
-Based on the human face detection adaboost classifier training procedures, written in MMATLAB, and we hope to share, has been tested.
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基于adaboost人脸检测，训练，-Based on the the adaboost human face detection, training,
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基于特征向量的人脸识别，有训练集和样本集，通过Adaboost强分类器算法实现，结果精确度达到95 以上，给定一个example,就可以在样本集中识别出对应的人脸。-Face recognition based on feature vectors have the training set and sample set by Adaboost strong classifier algorithm, the results of more than 95 accuracy, given an
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haar+adaboost人脸训练和检测源码,优化程序,速度极快-haar+adaboost face training and testing source code optimizer, fast
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this codes is Source code for face detection of viola paper.of its Features is:
Feature Computation: The “Integral” image representation
Feature Selection: The AdaBoost training algorithm .
Real-timeliness: A cascade of classifiers.-this code
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使用pca方法对图像进行特征提取，对训练集的20个人的共一百张人脸进行训练，使用adaboost算法生成强分类器，可以对测试集的人脸图片进行识别，且识别率较高(The PCA method is used to extract the features of the image, and the training is carried out for a total of 100 faces of 20 people in the training set. The AdaBoost algor
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