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HOG-adaboost

于 2014-04-09 发布 文件大小:9809KB
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下载积分: 1 下载次数: 42
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代码说明：

  HOG adaboost pedestrian-detect

文件列表：

HOG adaboost行人检测
....................\@crossvalidation
....................\................\CatFold.m,1162,2008-05-07
....................\................\crossvalidation.m,996,2008-05-07
....................\................\GetFold.m,1008,2008-05-07
....................\................\Initialize.asv,2403,2008-05-07
....................\................\Initialize.m,2400,2008-05-07
....................\@tree_node_w
....................\............\calc_output.asv,1399,2010-12-06
....................\............\calc_output.m,1399,2010-12-06
....................\............\do_learn_nu.asv,4941,2010-12-06
....................\............\do_learn_nu.m,4880,2010-12-06
....................\............\get_dim_and_tr.asv,1744,2008-05-07
....................\............\get_dim_and_tr.m,1964,2008-05-07
....................\............\train.asv,3665,2010-12-06
....................\............\train.m,3665,2010-12-06
....................\............\tree_node_w.m,1103,2010-11-25
....................\BinHOGFeature.m,1367,2010-12-01
....................\Classify.m,1345,2010-12-06
....................\GentleAdaBoost.m,3220,2010-11-30
....................\HOGGradient.m,972,2010-12-01
....................\ImgHOGFeature.m,2956,2010-12-02
....................\ImgHOGFeature3.m,2954,2010-12-08
....................\neg





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  • afdpf
    正反馈主动频率偏移法，是应用于孤岛检测的一种算法，可以减小检测盲区，加快孤岛检测速度(Positive feedback active frequency offset method is an algorithm used in islanding detection)
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    在visual c++中用分形完成的julia集,是分形的具体应用,对学分形的朋友应该有帮助(In the visual c++ Used to complete the julia fractal set, is the concrete application of the fractal, Friend of the credits should be shaped to help)
    2008-07-10 12:29:03下载
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  • blur
    本文是中值滤波的一些应用，个人觉得中值滤波比一些算子滤波效果要好得多(This article is median filtering applications, personal feel median filter is much better than some of the filtering effect of the operator)
    2012-10-09 20:26:27下载
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    根据RBM深度网络（Hinton，2006）进行MINST手写文字的分类器的训练，利用训练得到的权值制作了这个小程序，通过这个程序可以看出训练结果对数据集内的测试样本和训练样本都能进行很好的识别，但是对其他的手写字体识别就没有那么好了。(According to RBM depth network (Hinton, 2006) conducted MINST handwritten text classifier training, using the training to get the right value to produce this small program, this program can be seen through the results of the training data set and the training sample test samples are can be well identified, but on the other handwriting recognition is not so good.)
    2013-10-17 15:55:34下载
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    2021-02-02 22:49:59下载
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