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  • Features

    2011-07-08 12:06:17
    Features Features相当于网站的一个插件,甚至可以把网站定制成许多插件的组合。在c:\Program Files\Common Files\Microsoft Shared\web server extensions\12\template\features路径下
    Features
     
     
    Features相当于网站的一个插件,甚至可以把网站定制成许多插件的组合。在c:\Program Files\Common Files\Microsoft Shared\web server extensions\12\template\features路径下,能够找到系统所有的features,每个feature都对应一个目录,每个目录下都包含一个feature.xml的文件。
    Features的使用范围可以使场、Web应用程序、网站集、网站。
    打开“网站操作”→“网站设置” →“修改所有网站设置”,在网站设置的页面中,单击“网站集功能”命令,能够看到有很多系统内置的功能
     
     
     
    某些Feature必须要激活,否则,网站的很多功能用不了。如Office SharePoint Server标准版网站集功能、Office SharePoint Server发布基础架构、Office SharePoint Server企业版网站集功能这三个feature是必须激活的
     
     
    Feature架构    
      使用VS2008创建一个类库, 创建feature.xml
    <Feature
     ActivateOnDefault="True"|"FALSE"
     AlwaysForceInstall="TRUE"|"FALSE"
     AutoActivateInCentralAdmin="TRUE"|"FALSE"
     Creator="Text"
     DefaultResourceFile="Text"
     Description="Text"
     Hidden="TRUE"|"FALSE"
     ID="Text"
     ImageUrl="Text"
     ImageUrlAltText="Text"
     ReceiverAssembly="Text"
     RequireResources="TRUE"|"FALSE"
     Scope="Text"
     SolutionId="Text"
     Title="Text"
     Version="Text">
    </Feature>
     
     
     
     
    v其他属性可以参考WSS3.0SDK
     
    在Feature.xml文件中,Feature元素定义了其本身,并指定了相关的程序集、文件、依赖等,或者支持该feature的一些属性。Feature.xml文件的结构如下:
    Feature
    ActivationDependencies
          ActivationDependency
     ElementManifests
            ElementFile
            ElementManifest
     Properties
             Property
     
     子元素ElementManifests包含一些文件,主要是Feature的元素清单和定义文件。ElementManifests子元素指定一个包含ElementManifest元素和ElementFile元素。
    <ElementManifest Location=“Text”></ElementManifest>
    Location属性:指定一个包含Feature元素定义的文件相对路径,如
    <ElementManifest Location=“elements.xml”/>
     
     
    添加自定义菜单
     
    在网站操作按钮的下拉菜单中增加一个自定义菜单,当用户单击后,出现自定义界面,这个功能可以使用Feature来完成
    1.feature.xml文件
    <Feature Id="AA929AFF-4602-4d7f-A501-B80AC9A4BB52"
       Title="这是我利用feature增加的site级别的菜单"
       Description="这是我利用feature增加的site级别的菜单"
       Scope="Site"
       xmlns="http://schemas.microsoft.com/sharepoint/">
     <ElementManifests>
      <ElementManifest Location="elements.xml"/>   
     </ElementManifests>
    </Feature>
     
    2.elements.xml
    <Elements xmlns="http://schemas.microsoft.com/sharepoint/">
     <CustomAction
          Id="mypage"
        GroupId="SiteActions"
        Location="Microsoft.SharePoint.StandardMenu"
        Sequence="200"
        Title="我的日常工作"
        Description="Getting up and going with inline code">
      <UrlAction Url="~/site/pages/request.aspx"/>
     </CustomAction>
    </Elements>
     

    3.Features的部署
    使用命令行工具stsadm,编写一个批处理文件,执行即可。
    echo Copying the feature...
    echo.
    rd /s /q "%CommonProgramFiles%\Microsoft Shared\web server extensions\12\TEMPLATE\FEATURES\custaction"
    mkdir "%CommonProgramFiles%\Microsoft Shared\web server extensions\12\TEMPLATE\FEATURES\custaction"

    copy /Y feature.xml  "%CommonProgramFiles%\Microsoft Shared\web server extensions\12\TEMPLATE\FEATURES\custaction\"
    copy /Y elements.xml  "%CommonProgramFiles%\Microsoft Shared\web server extensions\12\TEMPLATE\FEATURES\custaction\"
    echo.
    echo Activating the feature...
    echo.
    pushd %programfiles%\common files\microsoft shared\web server extensions\12\bin
    stsadm -o deactivatefeature -filename custaction\feature.xml -url http://mei:9000/
    stsadm -o uninstallfeature -filename custaction\feature.xml -force

    stsadm -o installfeature -filename custaction\feature.xml -force
    stsadm -o activatefeature -filename custaction\feature.xml -url http://mei:9000/
    pushd

     
     
    v安装后,打开网站操作->网站设置->修改所有网站设置->网站集,可以看到
     
     
     

    展开全文
  • SURF算法的经典原文,SURF (Speeded Up Robust Features)也是一种类似于SIFT的兴趣点检测及描述子算法。其通过Hessian矩阵的行列式来确定兴趣点位置,再根据兴趣点邻域点的Haar小波响应来确定描述子,其描述子大小...
  • SLAM Features detection/Description

    万次阅读 2019-12-14 12:37:28
    Features Features detection/Description From handcrafted to deep local features. G. Csurka, C. R. Dance, M. Humenberger. 2018. Project Detection Description AKAZE x MSURF/MLDB DART x x KAZ...

    Features

    Features detection/Description

    From handcrafted to deep local features. G. Csurka, C. R. Dance, M. Humenberger. 2018.

    Project Detection Description
    AKAZE x MSURF/MLDB
    DART x x
    KAZE x MSURF/MLDB
    LIOP/MIOP x
    LIFT (machine learning) x x
    MROGH x
    SIFT x x
    SURF x x
    SFOP x
    展开全文
  • Tandem Features or Bottleneck Features

    千次阅读 2016-09-13 17:08:32
    这两个词刚看到的时候没反应过来是什么意思,在 Deep Neural Network based Text-Dependent Speaker Recognition:Preliminary Results 这篇文章中,原文如下: Another approach that makes use of a phonetic ...

    这两个词刚看到的时候没反应过来是什么意思,在 Deep Neural Network based Text-Dependent Speaker Recognition:Preliminary Results 这篇文章中,原文如下:
    Another approach that makes use of a phonetic discriminant DNN for speaker verification is the so-called bottleneck or tandem features approach [8]. A DNN is trained in supervised mode using the triphone state labels as targets. Once the net-work is trained, deep features can be extracted for every speechframe of a recording. The dimension of the deep feature is usually kept the same as the spectral feature by means of a bottleneck layer. These features in turn are used to train a backend classifier like a GMM-UBM or PLDA. Tandem feature are formed by combining the deep feature and the spectral feature corresponding to a given speech frame.
    Tandem features have been successfully applied to both text-independent and text-dependent speaker verification [9, 10]. Inthe text-dependent case, the approach was tested on the multiplepass-phrase task. To our knowledge this works represents thesmallest dataset used for training neural net models.

    [8] TianfanFu,YanminQian,YuanLiu,andKaiYu,“Tandemdeep features for text-dependent speaker verification.,” inINTERSPEECH, 2014, pp. 1327–1331.
    瓶颈特征或者叫串联特征,前者是形象的说法,一般输入的维度要大于隐层的维度,通常是在最后一个隐层的时候减小到我们想要的维度,
    tandem-features
    图片来自[8], 下面接结合这个图说一下对tandem-feature 的理解:
    input layer: right-context=5 + left-context=5 + current frame =1 = 11 frame
    hidden layer: 如果只是为了提取特征那可以直接pre-trian, 最后一个隐层降到想要的维度, 但是这篇文章中:
    All the deep models have 7 hidden layers with 1024 nodes per layer. 实验结果是从第二层或者第四层取值做PCA 的效果比较好,这一部分叫deep feature ,然后和原始的current frame 的39维PLP feature,做一个concatenate,组成78维的tandem-deep-feature, 然后送到GMM-UBM中训练UBM模型,而传统的都是直接用PLP 或者 MFCC + VAD特征去训练UBM模型.
    output layer:是number of speakers, 比如在RSR2015数据集,用bkg和dev来训练DNN,在bkg和dev集中共194 speakers,所有speaker label应该是194维的01序列,然后就是phone label应该是对应的triphone的number, 这个集合中用GMM对齐的tied-triphone-states = 3001. 原文如下(有删减):
    The state alignment for the phone DNN training was generated using a GMM model with 3001 tiedtriphone-states, which is built on a 50-hour SWB English task and 194 classes (194 speakers in bkg and dev set) are used in the speaker DNN training.
    所以有如果是这种结构的话,输出节点个数应该是(194+3001=3195个节点).

    上图是一个phone + speaker DNN training model, 当然可以phone DNN training model 和 speaker DNN training model 可以单独用, 在[8] 中的实验结果可以看出,Once the neural network training process is finished, the output layers of the two multi-task joint-learned DNNs can be removed, and the rest of each of the neural networks (common hidden layers) is used to extract the speaker-text joint representative features.

    展开全文
  • ImageFeatures

    2018-10-11 22:41:55
    struct CV_EXPORTS ImageFeatures { int img_idx; Size img_size; std::vector keypoints; Mat descriptors; };

    struct CV_EXPORTS ImageFeatures
    {
    int img_idx;
    Size img_size;
    std::vector keypoints;
    Mat descriptors;
    };

    展开全文
  • Features Track

    2018-09-14 12:33:46
    Morgana is learning computer vision, and he likes cats, too. One day he wants to find the cat movement from a cat ... To do this, he extracts cat features in each frame. A cat feature is a two-dime...
  • Extracting Features

    2017-01-09 17:33:16
    Extracting Features In this tutorial, we will extract features using a pre-trained model with the included C++ utility. Note that we recommend using the Python interface for this task, as for examp
  • cpufeatures

    2012-05-26 19:46:17
    the source is in /media/LENOVO/src1/1/android-20120108-2.2.2_r1/ndk/sources/cpufeatures. http://blog.csdn.net/abnerchai/article/details/6830644
  • SIFT features

    千次阅读 2010-03-22 17:02:00
    SIFT featuresScale Invariant Feature Transform (SIFT) is an approach for detecting and extracting local feature descriptors that are reasonably invariant to change in illumination, image noise, rota
  • ASProtect Features

    2006-10-31 13:04:00
    ASProtect Features compression of the application encryption of the application counteraction to dumping application memory with the tool like ProcDump. application integrity
  • [图片说明](https://img-ask.csdn.net/upload/201506/09/1433858279_124245.png)想给我的Myeclipse安装spket,但总是报错:如图所示:显示在MyEclipse 10 文件夹下面找不到features文件夹中的某个文件,但是实际上...
  • k-means + Bag of features 源码

    热门讨论 2011-08-02 20:29:42
    这是一个用matlab和c++联合编写的bag of features源码,实现了完整的bof 图像搜索功能,经测试准确度可达80%.具体用法详见我的CSDN博客。具体用法详见我的CSDN博客。
  • bs4.FeatureNotFound: Couldn't find a tree builder with the features you requested: lxml. Do you need to install a parser library? 几经周折才知道是bs4调用了python自带的html解析器,我用的mac,默认安装的...
  • optimizer_features_enable

    千次阅读 2012-11-14 12:38:48
    if you upgrade your database from release 10.1 to release 11.1, but you want to keep the release 10.2.0.4 optimizer behavior, you can do so by setting this parameter to 10.2.0.4. At a later time, you
  • 不得不说,这深度学习框架更新太快了尤其到了Keras2.0版本,快到Keras中文版好多都是错的,快到官方文档也有旧的没更新,前路坑太多。 到发文为止,已经有theano/tensorflow/CNTK支持keras,虽然说tensorflow造势...
  • BoF-SIFT Features with OpenCV

    千次阅读 2013-07-20 23:11:16
    Content based image retrieval (CBIR) is still an active research field. There are number of approaches available to retrieve visual data from large databases. But almost all the approaches require an ...
  • Beginning Java 8 Language Features电子书
  • Bag of features

    万次阅读 热门讨论 2011-08-02 21:22:41
    matlab+VC 实现Bag of features     在实验室度过的时光果然比在家充实很多很多。这几天收获真的是很大。在此期间,我初步学习了一下用于图像检索的Bag of features(Bof)算法,并且编程实现了它。此算法的神奇...
  • Tamura texture features

    千次阅读 2012-06-12 14:30:47
    上图是灰度共生矩阵的原理图。 原理可以详细阅读有关论文,这里有详细介绍:查看链接 基于人类对纹理的视觉感知的心理学的研究,Tamura等人提出了纹理特征的表达[14]。Tamura纹理特征的六个分量对应于心理学...
  • fastjson Features 说明

    千次阅读 2018-10-19 10:39:02
    /**  * 这个特性,决定了解析器是否将自动关闭那些不属于parser自己的输入源。 如果禁止,则调用应用不得不分别去关闭那些被用来创建parser的基础输入流InputStream和reader;如果允许,parser只要自己需要获取...
  • Haralick texture features

    千次阅读 2018-04-01 10:18:05
    Haralick texture featuresHaralick's texture features [28] were calculated using the kharalick() function of the cytometry tool box [29] for Khoros (version 2.1 Pro, Khoral Research, Inc., ...
  • 希望定制活动页面的Title,onCreate中代码如下:final Window window = getWindow(); boolean useTitleFeature = false; if(window.getContainer() == null) { useTitleFeature = window.requestFeature(Window....
  • Spring Boot features

    2019-04-17 21:41:34
    https://docs.spring.io/spring-boot/docs/current/reference/html/boot-features-spring-application.html 此篇文章主要介绍了SpringBoot的主要功能: 1.通常通过SpringApplication.run方法启动; 2.SpringBoot有...
  • Windows Server 2012 - 添加.NET 3.5 features

    千次阅读 2013-10-14 13:44:43
    添加.NET 3.5 features, 走默认设置,安装失败,需要windows安装盘 并特别制定一个路径参考:...
  • Features and Characteristics

    千次阅读 2015-02-06 14:08:48
    功能和特点Google公布了关于Android的以下特性:应用程序框架(Application framework) 应用开发者们使用应用程序框架开发一种称之为Android应用的程序。...Dalvik虚拟机 Android使用了全新的字
  • NDK CPU Features

    千次阅读 2013-04-02 13:11:11
    Android NDK CPU Features detection library: ------------------------------------------- This NDK provides a small library named "cpufeatures" that can be used at runtime to detect the target device's
  • strings 提取 features

    千次阅读 2012-02-17 20:39:31
    strings 是 Linux 命令,提取可执行文件中的字符串,你找到一个 Feature 名字,其他 Feature 名字就在附近,许多 .src 是这样生成的
  • CXF之Features

    千次阅读 2010-08-04 16:31:00
    CXF之Features, 讲述如何自定义Feature.

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