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ZKL_classifier_final

于 2020-02-27 发布

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下载积分: 1 下载次数: 12

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代码说明：

说明：基于高光谱数据集PaviaU的数据降维与分类,基于核函数的主成分分析kpca测试(Dimension reduction and classification based on hyperspectral data set paviau, KPCA test based on kernel function principal component analysis)

文件列表：

ZKL_classifier_final\cnnapplygrads.m, 640 , 2018-08-01
ZKL_classifier_final\cnnbp.m, 2150 , 2017-07-05
ZKL_classifier_final\cnnff.m, 2497 , 2018-08-01
ZKL_classifier_final\cnnnumgradcheck.m, 3430 , 2014-05-11
ZKL_classifier_final\cnnsetup.m, 2574 , 2018-08-01
ZKL_classifier_final\cnntest.m, 193 , 2014-05-11
ZKL_classifier_final\cnntrain.m, 1356 , 2018-08-01
ZKL_classifier_final\cnn_start.m, 1175 , 2018-08-02
ZKL_classifier_final\cnn_test.m, 2565 , 2018-08-02
ZKL_classifier_final\data_lda_done.m, 417 , 2018-08-05
ZKL_classifier_final\data_pca_done.m, 686 , 2018-08-05
ZKL_classifier_final\data_undone.m, 137 , 2018-08-04
ZKL_classifier_final\expand.m, 2009 , 2017-07-05
ZKL_classifier_final\flipall.m, 86 , 2017-07-05
ZKL_classifier_final\getpixel.m, 613 , 2018-07-31
ZKL_classifier_final\KNN.m, 1139 , 2018-08-03
ZKL_classifier_final\knn_hughes_test.m, 2159 , 2018-08-05
ZKL_classifier_final\knn_test.m, 2159 , 2018-08-05
ZKL_classifier_final\kpcaFordata.m, 3090 , 2018-08-04
ZKL_classifier_final\kpca_test.m, 2025 , 2018-08-04
ZKL_classifier_final\lda.m, 3055 , 2018-08-04
ZKL_classifier_final\lda_test.m, 2246 , 2018-08-04
ZKL_classifier_final\main_cnn_untrain.m, 2168 , 2018-08-02
ZKL_classifier_final\main_final.m, 7499 , 2018-08-05
ZKL_classifier_final\PaviaU_gt.mat, 11005 , 2017-06-25
ZKL_classifier_final\PCA2.m, 2385 , 2018-08-03
ZKL_classifier_final\pca_test.m, 3421 , 2018-08-03
ZKL_classifier_final\sigm.m, 54 , 2017-07-05
ZKL_classifier_final\test_x.mat, 255321 , 2017-07-06
ZKL_classifier_final\test_y.mat, 250 , 2017-07-06
ZKL_classifier_final\trainednet.mat, 789979 , 2017-07-06
ZKL_classifier_final\train_x.mat, 1266538 , 2017-07-06
ZKL_classifier_final\train_y.mat, 428 , 2017-07-06
ZKL_classifier_final\zjtest.m, 132 , 2019-12-18
ZKL_classifier_final\程序说明.txt, 401 , 2018-08-05
ZKL_classifier_final, 0 , 2020-02-27

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Azi_sim
说明：數據繪圖時候有時候必須處理資料矩陣轉向，該編碼詳細說明如何處理矩陣在三維度空間下進行轉向，而不是利用Curve Fitting Tools(Data drawing sometimes has to deal with the data matrix steering, which details how to handle the matrix to steer in a three-dimensional space instead of using Curve Fitting Tools)

2020-06-22 19:40:02下载

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2010-04-28 16:05:05下载

积分：1
shuangchongJEPG
由于图像在经过一次 JPEG 压缩和双重 JPEG 压缩之后其 DCT 系数具有一定的相关性，通过对这种相关性进行分析，提出了一种基于 JPEG 图像双重压缩特性的图像篡改检测算法，并用此算法检测图像是否经过篡改和定位篡改区域。 (Since the image after a first JPEG compression JPEG compression and double its DCT coefficients having some relevance, by this correlation is analyzed, and an image based on JPEG image compression characteristic double tamper detection algorithm, and the algorithm used to detect whether the image been tampered with and locate the tampered areas.)

2021-04-02 22:29:07下载

积分：1
mixture_of_gaussians
视频图像处理，采用背景减除法中混合高斯建模进行视频前景和背景的分离(Video image processing, background subtraction method using Gaussian mixture modeling to separate the foreground and background video)

2014-11-02 14:26:14下载

积分：1
FingerPrint
指纹图像处理，细化，锐化，二值化，特征点的提取。(Fingerprint image processing, refining, sharpening, binarization, feature point extraction.)

2011-05-14 17:16:06下载

积分：1
super-resolutioncode
该代码是基于学习的超分辨率重建，此算法是在线字典学习，字典的训练与稀疏表示与重建同时进行。(This code is based on the study of super-resolution reconstruction, this algorithm is learning online dictionaries, dictionaries of training and sparse representation and reconstruction simultaneously.)

2016-03-10 09:06:50下载

积分：1
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读取MODIS的HDF文件和ENVI IDL的程序(MODIS ENVI IDL readhdf.pro)

2015-08-26 11:39:06下载

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讲解滤波与二值图像处理，比较简单的图像处理源码(Explain the filtering and binary image processing, image processing source code is relatively simple)

2011-12-27 19:14:54下载

积分：1
fm_gui_v2
基于傅里叶梅林变换亚像元图像配准程序，可以运行。(Fourier Mellin transform based sub-pixel image registration process, you can run.)

2011-04-24 16:00:32下载

积分：1
FSIM
Zhang等人利用相位一致性信息的特性，提出了特征相似性方法(FSIMG) ，选取了相位一致性信息和梯度信息作为它的两个特征，得到了较好的结果。 (A novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS’ perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA s demonstrate that FSIM can achieve much higher consistency with the subjective uations than state-of-the-art IQA metrics.)

2015-09-16 19:18:45下载

积分：1

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