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R语言 wavethresh包 imwd.object()函数中文帮助文档(中英文对照)

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发表于 2012-10-1 17:21:36 | 显示全部楼层 |阅读模式
imwd.object(wavethresh)
imwd.object()所属R语言包:wavethresh

                                        Two-dimensional wavelet decomposition objects.
                                         二维小波分解对象。

                                         译者:生物统计家园网 机器人LoveR

描述----------Description----------

These are objects of classes
这些类的对象

imwd
imwd

They represent a decomposition of an image with respect to a two-dimensional wavelet basis (or tight frame in the case of the two-dimensional (space-ordered) non-decimated wavelet decomposition).
它们代表了一个分解的图像相对于一个二维小波基础(或紧的情况下的帧中的两维(空间排列的)非抽取小波分解)。


Details

详细信息----------Details----------

In previous releases the original image was stored as the "original" component of a imwd object. This is not done now as the resulting objects were excessively large.
在以前的版本中,原始图像存储为“原始”的组成部分的imwd对象。现在不这样做所产生的物件过大。


值----------Value----------

The following components must be included in a legitimate "imwd" object.
以下组件必须被包括在一个合法imwd的对象。

<table summary="R valueblock"> <tr valign="top"><td>nlevels</td> <td> number of levels in wavelet decomposition. If you raise 2 to the power of nlevels then you get the dimension of the image that you originally started with. </td></tr> <tr valign="top"><td>type</td> <td> If type="wavelet" then the image was decomposed according to the 2D Mallat pyramidal algorithm. If type="station" then the image was decomposed using the 2D spatially ordered non-decimated wavelet transform.</td></tr> <tr valign="top"><td>fl.dbase</td> <td> The first last database associated with the decomposition. For images, this list is not very useful as each level's components is stored as a list component, rather than being packaged up in a single vector as in the 1D case. Nevertheless the internals still need to know about fl.dbase to get the computations correct. See the help for first.last if you are a masochist. </td></tr> <tr valign="top"><td>filter</td> <td> A filter object as returned by the filter.select function. This component records the filter used in the decomposition. The reconstruction routines use this component to find out what filter to use in reconstruction. </td></tr> <tr valign="top"><td>wNLx</td> <td> The object will probably contain many components with names of this form. These are all the wavelet coefficients of the decomposition. In "wNLx" the "N" refers to the level number and the "x" refers to the direction of the coefficients with "1" being horizontal, "2" being vertical and "3" being diagonal and "4" corresonding to scaling function coefficients at the given resolution level. Note that the levels should be in numerically decreasing order, so if nlevels is 5, then there will be w5L1, w5L2, w5L3 first, then down to w1L1, w1L2, and w1L3. Note that these coefficients store their data according to the first.last database fl.dbase$first.last.d, so refer to them using this.  Note that if type="wavelet" then images at level N are subimages of side length 2^N pixels. If the type component is "station" then each coefficient subimage is of the same dimension as the input image used to create this object.</td></tr> <tr valign="top"><td>w0Lconstant</td> <td> This is the coefficient of the bottom level scaling function coefficient. So for examples, if you used Haar wavelets this would be the sample mean of the data (scaled by some factor depending on the number of levels, nlevels).</td></tr> <tr valign="top"><td>bc</td> <td> This component details how the boundaries were treated in the decomposition.</td></tr>  </table>
<table summary="R valueblock"> <tr valign="top"> <TD>nlevels</ TD> <TD>在小波分解的层次。如果你提高2的力量NLEVELS的,那么你的形象,你最初的尺寸。 </ TD> </ TR> <tr valign="top"> <TD> type </ TD> <TD>如果type="wavelet"然后将图像分解的二维Mallat的金字塔算法。如果type="station"然后将图像分解二维空间下令非抽取小波变换。</ TD> </ TR> <tr valign="top"> <TD> fl.dbase</ TD> <TD>的第一个相关联的数据库的分解。对于图像来说,这份名单是不是非常有用,因为每个级别的存储组件的列表组件,而不是在一维的情况下,被打包成一个向量。然而,内部仍然需要知道关于fl.dbase的计算是正确的。请参阅帮助first.last如果你是受虐狂。 </ TD> </ TR> <tr valign="top"> <TD> filter </ TD> <td>一个过滤器作为返回filter.select功能的对象。此组件记录在分解中使用的过滤器。重建程序使用这个组件,以找出什么样的滤波器中使用的重建。 </ TD> </ TR> <tr valign="top"> <TD>wNLx</ TD> <TD>的对象可能会包含许多组件,这种形式的名称。这些所有的小波系数的分解。在“N”是指在“wNLx”级别号的“x”指的方向的系数中包含“1”的水平,“2”,垂直和对角线和“4”3“”corresonding缩放在给定的分辨率级别的函数系数。注意:水平应该是在数字递减的顺序,,所以如果NLEVELS是5,那么就会是w5L1,w5L2,w5L3,然后下降到W1L1,w1L2,w1L3。请注意,这些系数将其数据存储的first.last数据库fl.dbase$first.last.d,所以,请参阅使用。请注意,如果type="wavelet"然后图像在第N级子图像的边长2^N像素。如果该类型组件是"station"然后每个子图是用于创建此对象的输入图像尺寸相同的系数。</ TD> </ TR> <tr valign="top"> <TD>w0Lconstant </ TD> <td>这是最底层的尺度函数系数系数。所以的例子,如果你使用Haar小波这将是样本平均数的数据缩放的水平,NLEVELS的数量取决于一些因素。</ TD> </ TR> <tr valign="top"> TD> bc </ TD> <td>此组件的详细信息分解的界限如何处理。</ TD> </ TR> </ TABLE>


GENERATION----------GENERATION----------

This class of objects is returned from the imwd function to represent a two-dimensional (possibly space-ordered non-decimated) wavelet decomposition of a function. Many other functions return an object of class imwd.
这个类的对象,则返回从imwd函数来表示一个二维(可能空间排列的非抽取的一个函数)小波分解。许多其他的函数返回一个类imwd对象的。


方法----------METHODS----------

The imwd class of objects has methods for the following generic functions: compress, draw, imwr, nullevels.imwd, plot, print, summary, threshold.imwd.
的imwd类的对象有以下通用功能的方法:compress,draw,imwr,nullevels.imwd,plot,print, summary,threshold.imwd。


RELEASE----------RELEASE----------

Version 3.5.3 Copyright Guy Nason 1994
版本3.5.3版权盖利晨1994年


(作者)----------Author(s)----------


G P Nason



参见----------See Also----------

imwd
imwd

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


注:
注1:为了方便大家学习,本文档为生物统计家园网机器人LoveR翻译而成,仅供个人R语言学习参考使用,生物统计家园保留版权。
注2:由于是机器人自动翻译,难免有不准确之处,使用时仔细对照中、英文内容进行反复理解,可以帮助R语言的学习。
注3:如遇到不准确之处,请在本贴的后面进行回帖,我们会逐渐进行修订。
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