登录
首页 » matlab » SNS_matlab-master

SNS_matlab-master

于 2021-05-14 发布 文件大小:12485KB
0 347
下载积分: 1 下载次数: 2
我要下载

代码说明：

  图像重定向源代码，本文提出一种网格变形框架，允许显著特征进行均匀缩放，从而保证图像内容的完整性，将变形分布到不重要的区域。(The source code of optimal scale stretching image redirection is presented, and an effective image redirection algorithm is proposed to better guarantee the integrity of the image.)

文件列表：

SNS_matlab-master, 0 , 2018-10-24
SNS_matlab-master\Brasserie_L_Aficion.png, 1056989 , 2016-06-27
SNS_matlab-master\Brasserie_L_Aficion_0.50_sns.png, 460700 , 2016-06-27
SNS_matlab-master\Demo.m, 2318 , 2018-07-12
SNS_matlab-master\gbvs, 0 , 2018-10-24
SNS_matlab-master\gbvs\algsrc, 0 , 2018-10-24
SNS_matlab-master\gbvs\algsrc\connectMatrix.m, 2668 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\distanceMatrix.m, 2137 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\formMapPyramid.m, 942 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\getDims.m, 110 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\graphsalapply.m, 1997 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\graphsalinit.m, 1026 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\indexmatrix.m, 104 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\initGBVS.m, 1772 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\makeLocationMap.m, 945 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexArrangeLinear.cc, 1342 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexArrangeLinear.mexa64, 6982 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexArrangeLinear.mexglx, 5265 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexArrangeLinear.mexmaci, 8816 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexArrangeLinear.mexmaci64, 8736 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexArrangeLinear.mexw32, 6144 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexArrangeLinear.mexw64, 8192 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexAssignWeights.cc, 1836 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexAssignWeights.mexa64, 7240 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexAssignWeights.mexglx, 5325 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexAssignWeights.mexmaci, 8796 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexAssignWeights.mexmaci64, 8720 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexAssignWeights.mexw32, 6144 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexAssignWeights.mexw64, 7680 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexColumnNormalize.cc, 645 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexColumnNormalize.mexa64, 6758 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexColumnNormalize.mexglx, 4945 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexColumnNormalize.mexmaci, 8780 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexColumnNormalize.mexmaci64, 8704 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexColumnNormalize.mexw32, 6144 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexColumnNormalize.mexw64, 7680 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexSumOverScales.cc, 1358 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexSumOverScales.mexa64, 6870 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexSumOverScales.mexglx, 5169 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexSumOverScales.mexmaci, 8816 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexSumOverScales.mexmaci64, 8736 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexSumOverScales.mexw32, 6144 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexSumOverScales.mexw64, 8192 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexVectorToMap.cc, 938 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexVectorToMap.mexa64, 6597 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexVectorToMap.mexglx, 4900 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexVectorToMap.mexmaci, 8748 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexVectorToMap.mexmaci64, 8680 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexVectorToMap.mexw32, 6144 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\mexVectorToMap.mexw64, 7680 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\namenodes.m, 334 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\partitionindex.m, 365 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\principalEigenvectorRaw.m, 570 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\simpledistance.m, 1000 , 2016-06-27
SNS_matlab-master\gbvs\algsrc\sparseness.m, 63 , 2016-06-27
SNS_matlab-master\gbvs\compile, 0 , 2018-10-24
SNS_matlab-master\gbvs\compile\cleanmex.m, 12 , 2016-06-27
SNS_matlab-master\gbvs\compile\gbvs_compile.m, 303 , 2016-06-27
SNS_matlab-master\gbvs\compile\gbvs_compile2.m, 351 , 2016-06-27
SNS_matlab-master\gbvs\demo, 0 , 2018-10-24
SNS_matlab-master\gbvs\demo\demonstration.m, 1693 , 2016-06-27
SNS_matlab-master\gbvs\demo\flicker_motion_demo.m, 744 , 2016-06-27
SNS_matlab-master\gbvs\demo\simplest_demonstration.m, 1812 , 2016-06-27
SNS_matlab-master\gbvs\gbvs.m, 9683 , 2016-06-27
SNS_matlab-master\gbvs\gbvs_fast.m, 344 , 2016-06-27
SNS_matlab-master\gbvs\gbvs_install.m, 100 , 2016-06-27
SNS_matlab-master\gbvs\initcache, 0 , 2018-10-24
SNS_matlab-master\gbvs\initcache\17__24__m__2.mat, 54381 , 2016-06-27
SNS_matlab-master\gbvs\initcache\18__24__m__2.mat, 60929 , 2016-06-27
SNS_matlab-master\gbvs\initcache\18__32__m__2.mat, 121042 , 2016-06-27
SNS_matlab-master\gbvs\initcache\19__24__m__2.mat, 68419 , 2016-06-27
SNS_matlab-master\gbvs\initcache\19__32__m__2.mat, 133276 , 2016-06-27
SNS_matlab-master\gbvs\initcache\20__32__m__2.mat, 146954 , 2016-06-27
SNS_matlab-master\gbvs\initcache\21__32__m__2.mat, 161673 , 2016-06-27
SNS_matlab-master\gbvs\initcache\22__32__m__2.mat, 179086 , 2016-06-27
SNS_matlab-master\gbvs\initcache\23__24__m__2.mat, 97815 , 2016-06-27
SNS_matlab-master\gbvs\initcache\23__32__m__2.mat, 194389 , 2016-06-27
SNS_matlab-master\gbvs\initcache\24__18__m__2.mat, 53317 , 2016-06-27
SNS_matlab-master\gbvs\initcache\24__23__m__2.mat, 95589 , 2016-06-27
SNS_matlab-master\gbvs\initcache\24__24__m__2.mat, 105818 , 2016-06-27
SNS_matlab-master\gbvs\initcache\24__32__m__2.mat, 207963 , 2016-06-27
SNS_matlab-master\gbvs\initcache\25__32__m__2.mat, 233546 , 2016-06-27
SNS_matlab-master\gbvs\initcache\26__32__m__2.mat, 255834 , 2016-06-27
SNS_matlab-master\gbvs\initcache\27__32__m__2.mat, 275933 , 2016-06-27
SNS_matlab-master\gbvs\initcache\27__40__m__2.mat, 493321 , 2016-06-27
SNS_matlab-master\gbvs\initcache\28__32__m__2.mat, 298538 , 2016-06-27
SNS_matlab-master\gbvs\initcache\29__30__m__2.mat, 276057 , 2016-06-27
SNS_matlab-master\gbvs\initcache\30__28__m__2.mat, 250817 , 2016-06-27
SNS_matlab-master\gbvs\initcache\30__29__m__2.mat, 274672 , 2016-06-27
SNS_matlab-master\gbvs\initcache\30__40__m__2.mat, 634888 , 2016-06-27
SNS_matlab-master\gbvs\initcache\32__16__m__2.mat, 67360 , 2016-06-27
SNS_matlab-master\gbvs\initcache\32__21__m__2.mat, 139087 , 2016-06-27
SNS_matlab-master\gbvs\initcache\32__24__m__2.mat, 183556 , 2016-06-27
SNS_matlab-master\gbvs\initcache\32__25__m__2.mat, 216061 , 2016-06-27
SNS_matlab-master\gbvs\initcache\32__28__m__2.mat, 272113 , 2016-06-27
SNS_matlab-master\gbvs\initcache\32__31__m__2.mat, 374266 , 2016-06-27
SNS_matlab-master\gbvs\initcache\32__32__m__2.mat, 387401 , 2016-06-27
SNS_matlab-master\gbvs\initcache\35__40__m__2.mat, 925911 , 2016-06-27
SNS_matlab-master\gbvs\initcache\40__30__m__2.mat, 603024 , 2016-06-27
SNS_matlab-master\gbvs\initcache\40__38__m__2.mat, 1135590 , 2016-06-27

下载说明：请别用迅雷下载，失败请重下，重下不扣分！

发表评论

0 个回复

  • cs,rd,rma
    用于雷达成像的三种不同算法，分别为CS算法、RD算法、RMA算法(cs ,rd ,rma algorithm)
    2021-04-27 20:38:44下载
    积分：1
  • 图像融合算法
    针对电气设备同一场景的红外与可见光图像间一致特征难以提取和匹配的问题，提出了一种基于斜率一致性的配准方法。首先通过数学形态学方法分别提取红外与可见光图像的边缘，得到粗边缘图像；然后通过SURF算法提取两幅边缘图像的特征点，根据正确的匹配点对之间斜率一致性的先验知识，进行特征点匹配；最后通过最小二乘法求得仿射变换模型参数并实现两幅图像的配准。实验结果表明，该方法有效提高了匹配点对的正确率，能够对电气设备红外和可见光图像实现高精度的配准。(In this paper, a method based on slope consistency is proposed to solve the problem that the uniform features of infrared and visible images are difficult to extract and match. Firstly, the edges of infrared and visible images were extracted by mathematical morphology, and the rough edge images were obtained. Then, the feature points of two edge images were extracted by SURF algorithm, and the priori knowledge of slope consistency was obtained according to the correct matching point, and the feature points were matched. Finally, the model parameters of affine transformation are obtained by least square method and the registration of two images is realized. The experimental results show that this method can effectively improve the accuracy of matching points, and can realize high precision registration of infrared and visible images of electrical equipment.)
    2018-03-24 10:53:41下载
    积分：1
  • LinearClassifier_1.0
    基于Fisher线性判别基础学习器的集成分类器专门用于数字媒体的隐写分析，目前使用高维特征空间。目前，由于其良好的检测精度和较小的计算成本，它可能是设计监督分类器用于数字图像隐写分析的最常用方法。(The ensemble classifier, based on Fisher Linear Discriminant base learners, was introduced specifically for steganalysis of digital media, which currently uses high-dimensional feature spaces. Presently it is probably the most used method to design supervised classifier for steganalysis of digital images because of its good detection accuracy and small computational cost. It has been assumed by the community that the classifier implements a non-linear boundary through pooling binary decision of individual classifiers within the ensemble.)
    2021-01-17 10:38:45下载
    积分：1
  • bt656
    生成bt656数据格式，针对视频adv7127芯片(Generate bt656 data format,)
    2017-08-30 18:12:58下载
    积分：1
  • 基于信息熵的约简MATLAB代码
    利用matlab和ceemd进行编程求解简单的信息熵，根据求解的imf分量判断信号的情况(Using MATLAB and ceemd programming to solve simple information entropy, according to the IMF component to determine the situation of the signal)
    2017-07-08 15:00:24下载
    积分：1
  • histeq
    说明：  实现直方图规定化的函数和直方图均衡化的函数(Histogram specification to achieve the function and the histogram equalization function)
    2010-04-19 18:56:53下载
    积分：1
  • BMPTEst
    实现在图像背景上叠加 下雨 下雪 的效果(Achieve in the BMP image background superimposed rain, snow effect)
    2017-07-19 11:54:09下载
    积分：1
  • zhongzhilvbo
    说明：  对图像进行中值滤波，代码调试通过，附有lena图像(The image is filtered by median filter, the code is debugged and passed, and the Lena image is attached.)
    2019-03-13 14:57:27下载
    积分：1
  • Desktop
    适用于任意图像中轮廓的直线段检测与描绘，对于公路车道直线检测具有良好效果。(Linear segment detection and description for contours in arbitrary Images,it has good effect for straight line detection of highway lanes.)
    2020-06-19 22:20:02下载
    积分：1
  • mage segmentation-IA
    说明：  利用经典免疫遗传算法实现单阈值图像的分割。(Classical immune genetic algorithm is used to segment single threshold image.)
    2019-03-18 21:00:32下载
    积分：1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载

热门标签

Visual C++ matlab C/C++ Java Others Unix_Linux EasyLanguage易语言 Visual Basic C# PDF C++ Delphi VHDL ASP WINDOWS
热门下载

Copyright© 2019-2026 | Powered by Imdn | 浙ICP备16007336号 | IMDN交流 | 联系客服（QQ：3324461878）

本网站资源收集自网络及网站用户上传，若有侵权请邮件至（3324461878@qq.com），核实后我们会在24小时内删除，谢谢！