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cnn

于 2021-05-14 发布 文件大小:14551KB
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代码说明：

  用于图像分类的源代码，深度学习的应用，cnn卷积神经网络的使用，可以自己更换数据集或者调优，适合初学者使用！(For image classification of the source code, the application of in-depth learning, cnn convolutional neural network, you can replace the data set or tuning, suitable for beginners!)

文件列表：

cnn识别英文字符 - 副本
......................\cnnapplygrads.m,744,2013-12-07
......................\cnnbp.m,3966,2013-12-16
......................\cnnexamples.m,2849,2014-01-04
......................\cnnff.m,3864,2013-12-16
......................\cnnff2.m,3980,2013-12-16
......................\cnnsample.m,3621,2013-12-04
......................\cnnsample_letters.m,6663,2014-01-04
......................\cnnsample_nums.m,3377,2013-12-16
......................\cnnsetup.m,5235,2013-11-26
......................\cnntest.m,943,2013-12-16
......................\cnntrain.m,2276,2013-12-16
......................\data
......................\....\letters





......................\....\.......\102.bmp,3078,2009-05-09
......................\....\.......\103.bmp,11574,2009-05-09
......................\....\.......\104.bmp,2406,2009-05-09
......................\....\.......\105.bmp,2274,2009-05-09
......................\....\.......\106.bmp,2094,2009-05-09
......................\....\.......\107.bmp,2394,2009-05-09
......................\....\.......\108.bmp,1494,2009-05-09
......................\....\.......\109.bmp,882,2009-05-10
......................\....\.......\11.bmp,3918,2009-05-09
......................\....\.......\110.bmp,7854,2009-05-09
......................\....\.......\111.bmp,4170,2009-05-09
......................\....\.......\112.bmp,3322,2009-05-09
......................\....\.......\113.bmp,11718,2009-05-09
......................\....\.......\114.bmp,3246,2009-05-09
......................\....\.......\115.bmp,2334,2009-05-09
......................\....\.......\116.bmp,1510,2009-05-10
......................\....\.......\117.bmp,1678,2009-05-10
......................\....\.......\118.bmp,1398,2009-05-10
......................\....\.......\119.bmp,694,2009-05-10
......................\....\.......\12.bmp,2230,2009-05-10
......................\....\.......\120.bmp,7594,2009-05-09
......................\....\.......\121.bmp,4838,2009-05-09
......................\....\.......\122.bmp,3438,2009-05-09
......................\....\.......\123.bmp,14502,2009-05-07
......................\....\.......\124.bmp,3250,2009-05-09
......................\....\.......\125.bmp,2934,2009-05-09
......................\....\.......\126.bmp,1666,2009-05-10
......................\....\.......\127.bmp,2934,2009-05-09
......................\....\.......\128.bmp,2034,2009-05-09
......................\....\.......\129.bmp,1094,2009-05-10
......................\....\.......\13.bmp,12634,2009-05-07
......................\....\.......\130.bmp,6858,2009-05-09
......................\....\.......\131.bmp,5390,2009-05-09
......................\....\.......\132.bmp,2298,2009-05-10
......................\....\.......\133.bmp,13314,2009-05-07
......................\....\.......\134.bmp,2614,2009-05-09
......................\....\.......\135.bmp,2814,2009-05-07
......................\....\.......\136.bmp,2678,2009-05-09
......................\....\.......\137.bmp,2014,2009-05-09
......................\....\.......\138.bmp,2214,2009-05-09
......................\....\.......\139.bmp,2022,2009-05-07
......................\....\.......\14.bmp,2874,2009-05-09
......................\....\.......\140.bmp,6886,2009-05-09
......................\....\.......\141.bmp,3854,2009-05-09
......................\....\.......\142.bmp,3626,2009-05-09
......................\....\.......\143.bmp,12566,2009-05-07
......................\....\.......\144.bmp,2430,2009-05-10
......................\....\.......\145.bmp,2154,2009-05-09
......................\....\.......\146.bmp,1158,2009-05-10
......................\....\.......\147.bmp,1398,2009-05-10
......................\....\.......\148.bmp,1302,2009-05-10
......................\....\.......\149.bmp,774,2009-05-10
......................\....\.......\15.bmp,2166,2009-05-10
......................\....\.......\150.bmp,5462,2009-05-10
......................\....\.......\151.bmp,6966,2009-05-06
......................\....\.......\152.bmp,3318,2009-05-09
......................\....\.......\153.bmp,11254,2009-05-07
......................\....\.......\154.bmp,1902,2009-05-10
......................\....\.......\155.bmp,2486,2009-05-09
......................\....\.......\156.bmp,1734,2009-05-10
......................\....\.......\157.bmp,1446,2009-05-10
......................\....\.......\158.bmp,1206,2009-05-10
......................\....\.......\159.bmp,934,2009-05-10
......................\....\.......\16.bmp,2434,2009-05-09
......................\....\.......\160.bmp,5814,2009-05-09
......................\....\.......\161.bmp,5850,2009-05-06
......................\....\.......\162.bmp,4270,2009-05-06
......................\....\.......\163.bmp,13670,2009-05-07
......................\....\.......\164.bmp,2294,2009-05-09
......................\....\.......\165.bmp,1874,2009-05-09
......................\....\.......\166.bmp,1974,2009-05-09
......................\....\.......\167.bmp,1974,2009-05-09
......................\....\.......\168.bmp,1398,2009-05-09
......................\....\.......\169.bmp,1790,2009-05-09
......................\....\.......\17.bmp,1974,2009-05-09
......................\....\.......\170.bmp,6210,2009-05-09
......................\....\.......\171.bmp,4170,2009-05-09
......................\....\.......\172.bmp,3330,2009-05-09
......................\....\.......\173.bmp,10934,2009-05-09
......................\....\.......\174.bmp,2394,2009-05-09
......................\....\.......\175.bmp,2230,2009-05-09

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    2019-04-02 15:24:23下载
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    2021-04-15 09:01:41下载
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  • zhongxin
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    2021-04-09 17:29:01下载
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    在本文件中，我主要研究通过行人的表观特征进行识别，例如颜色、纹理等等。用matlab GUI 编写了程序，对行人进行匹配 (Pedestrian recognition of the inter-camera is intelligent surveillance camera network a key research direction. Match the pedestrian different camera, you can cross the pedestrian track of the camera, the expansion of the monitoring horizon, at the same time can have a clearer grasp of the behavior of pedestrians in the entire surveillance area. Different cameras, different lighting conditions, the pedestrian position and a different scale, shelter, etc., making the pedestrian match challenging. In this document, I study to identify by pedestrian apparent characteristics such as color, texture, and so on.)
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    压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为OMP算法，对256*256的lena图处理，比较原图和OMP算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间 (Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix and OMP algorithm as the reconstruction algorithm. Compare lena figure and the reconstruction results using OMP algorithm at different sampling ratio (0.74,0.50.3),then each runs 50 times, compare the performance of PSNR and each running time)
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