高斯滤波在Matlab中的矢量 [英] Gaussian Filter on a vector in Matlab

问题描述

我有一个n维向量（1xn数据集，它不是图像数据），我想应用一个高斯滤波器。我有图像处理工具包和其他一些（询问你是否需要一个列表）。

假设我可以使 hsize > > fspecial 函数的参数，类似于 [1 n] 。
我是否仍然可以使用 imfilter 将其应用到我的向量中，或者我应该使用其他的方法吗？

我已经看到了很多关于如何在Matlab中将高斯滤波应用于二维图像数据的例子，但对于Matlab作为一个平台，我还是比较新的，所以一个例子会非常好。 / p>

注意：我目前还没有试过，看看会发生什么（当前没有安装Matlab的机器上），否则我会先试一下并只问我是否遇到问题，使用 fspecial 和 imfilter 。

解决方案

为什么不自己创建高斯过滤器？你可以看看 fspecial （或其他任何高斯定义）的公式：

  sigma = 5; 
 sz = 30; ％gaussFilter向量的长度
 x = linspace（-sz / 2，sz / 2，sz）; 
 gaussFilter = exp（-x。^ 2 /（2 * sigma ^ 2））; 
 gaussFilter = gaussFilter / sum（gaussFilter）; ％normalize 
  

为了应用它，您可以使用 filter ：

  y = rand（500,1）; 
 yfilt = filter（gaussFilter，1，y）; 
  

不要忘记滤波器有延迟，这意味着滤波后的信号相对于输入信号。由于此过滤器是对称的，因此可以通过使用 conv 而不是 filter 来获得未转换的输出，并使用相同选项：

  yfilt = conv（y，gaussFilter， ）; 
  

I have a n-dimensional vector (1xn dataset, and it is not image data), and I want to apply a Gaussian filter to it. I have the Image Processing Toolkit, and a few others (ask if you need a list).

Presumably I can make the hsize parameter of the fspecial function something like [1 n]. Can I still use imfilter to apply it to my vector as the next step, or should I be using something else?

I've seen quite a few examples on how to apply a Gaussian filter to two dimensional image data in Matlab, but I'm still relatively new to Matlab as a platform so an example would be really good.

Note: I'm not currently in a position to just try it and see what happens (not currently on a machine with Matlab installed), otherwise I would have tried it first and only asked if I ran into problems using fspecial and imfilter.

解决方案

Why not create the Gaussian filter yourself? You can look at the formula in fspecial (or any other definition of a Gaussian):

sigma = 5;
sz = 30;    % length of gaussFilter vector
x = linspace(-sz / 2, sz / 2, sz);
gaussFilter = exp(-x .^ 2 / (2 * sigma ^ 2));
gaussFilter = gaussFilter / sum (gaussFilter); % normalize

and in order to apply it you can use filter:

y = rand(500,1);
yfilt = filter (gaussFilter,1, y);

and don't forget the filter has latency, which means the filtered signal is shifted as compared to the input signal. Since this filter is symmetric, you can get a non-shifted output by using conv instead of filter, and use the same option:

yfilt = conv (y, gaussFilter, 'same');

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