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falpha

于 2006-12-26 发布 文件大小:1KB
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代码说明:

  一个计算多重分形谱的matlab程序,希望对大家有用。(a calculation of the multi-fractal spectrum of Matlab procedures in the hope that it may be useful right.)

文件列表:

falpha.m

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  • katsevich-recreat
    三维图像重建,采用多种滤波方法,实现三维图像重建,该算法效率比较高。目前已经应用到实际工程。(3-dim picture recreat,use filter method and project method to realise the recreating of 3 dim picture.it is a very effective method ,applied in the project.so I share it with you . )
    2016-08-26 18:23:53下载
    积分:1
  • Region_growing
    图像分割方法区域增长法,基于区域增长的感兴趣区域分割方法,区域生长是一种根据事先定义好的准则将像素或者子区域聚合成更大区域的过程(Image segmentation algorithm region growth method)
    2018-04-26 08:57:47下载
    积分:1
  • cvap3.7
    聚类有效性指标,比较全,功能比较强大,是学习聚类的好工具(Clustering Validity Indices)
    2021-04-10 09:18:59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
  • super-resolution
    对模糊的图像进行超分辨复原,运行后可使图像比单纯的插值更加清楚(Blurred image of the super-resolution restoration, running make images more clear than the simple interpolation)
    2010-05-09 18:15:53下载
    积分:1
  • matlab2
    matlab的四种边缘提取办法比较(代码)(the four edge detection matlab comparison approach (code))
    2009-06-01 19:30:53下载
    积分:1
  • 车牌定位系统程序
    车牌识别程序源码,很有用的!我也是从网上下载下来的,不过我自已做了些修改!(plates source identification procedures, very useful! I also downloaded from the Internet, but I do own some changes!)
    2020-06-26 10:20:02下载
    积分:1
  • internet
    这是学习CVI关于网络设置方面的一个很有价值的例子(to study how to use cvi)
    2010-01-12 21:27:14下载
    积分:1
  • cv-project
    该文章包含了计算机视觉方面的论文及代码,如目标追踪、目标检测、人脸识别、机器学习、分割等(this document contains several papers and codes,such as object tracking、object detection、face recognition and so on)
    2017-04-04 22:17:41下载
    积分:1
  • LinearClassifier_1.0
    说明:  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
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