Question about convolutions

  • Thread starter AxiomOfChoice
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In summary, if you are convolving two functions, and want to take the Fourier transform of the product, you need to first do the Fourier transforms of the individual functions, and then take the back transform.
  • #1
AxiomOfChoice
533
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I know that the convolution of two functions [itex]f(x)[/itex] and [itex]g(x)[/itex] is given by

[tex]
(f * g)(y) = \int_{\mathbb R} f(x)g(y-x) dx.
[/tex]

But what if I'm trying to convolve a function [itex]f(x)[/itex] with a function [itex]g(x + az)[/itex], where [itex]a[/itex] is some constant? Is it just

[tex]
(f*g)(y) = \int_{\mathbb R} f(x)g(y - x + az) dx.
[/tex]

If so, why? I can't seem to find a definition of the convolution that makes this obvious.
 
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  • #2
Convolutions are symmetric in f and g:

$$(f \star g)(y) = (g \star f)(y);$$

you could thus write the convolution of f(x) and g(x + az) as

$$\int_{\mathbb{R}} dx~f(y-x)g(x+az),$$

which is perhaps easier to see.
 
  • #3
Your question is a little puzzling. How does z relate to x in g(x+az)?
 
  • #4
Okay, let me see if I can be more specific. What I'm really trying to do is to take the Fourier transform...in the [itex]x[/itex] variable...of the product [itex]f(x) g(ax + y)[/itex], where [itex]x,y\in \mathbb R[/itex] are independent (hence unrelated) variables. So I'm interested in

[tex]
\int_{-\infty}^{\infty} e^{-i p x} f(x) \cdot g(ax + y) \ dx.
[/tex]

I know that Fourier transforms turn products into convolutions...but is it that straightforward in this case?
 
  • #5
AxiomOfChoice said:
Okay, let me see if I can be more specific. What I'm really trying to do is to take the Fourier transform...in the [itex]x[/itex] variable...of the product [itex]f(x) g(ax + y)[/itex], where [itex]x,y\in \mathbb R[/itex] are independent (hence unrelated) variables. So I'm interested in

[tex]
\int_{-\infty}^{\infty} e^{-i p x} f(x) \cdot g(ax + y) \ dx.
[/tex]

I know that Fourier transforms turn products into convolutions...but is it that straightforward in this case?

What you need is the opposite. Fourier transform of a convolution is product of Fourier transforms of the individual items. Once you've done that, take the back transform to get what you are looking for.
 

What are convolutions and why are they important in science?

Convolutions are mathematical operations that are used to analyze and manipulate data, particularly in the fields of signal processing and image recognition. They are important because they allow us to extract features and patterns from data, which can then be used to make predictions and decisions.

How are convolutions used in image recognition and computer vision?

In image recognition and computer vision, convolutions are used to extract features from images. This involves sliding a filter (a small matrix of numbers) over an image and computing the dot product between the filter and the corresponding section of the image. This process is repeated multiple times, creating a new image with the extracted features.

What is the difference between 1D, 2D, and 3D convolutions?

The number before "D" refers to the dimensionality of the data being processed. 1D convolutions are used for sequential data, such as text or audio signals. 2D convolutions are used for 2D data, such as images. 3D convolutions are used for 3D data, such as video. The main difference between these types of convolutions is the shape and structure of the filter used.

How do convolutions benefit machine learning and artificial intelligence?

Convolutions are a key component in many machine learning and artificial intelligence algorithms. They are used to extract features and patterns from data, which can then be used to train models and make predictions. This allows machines to learn and make decisions based on the data they are given.

What are some real-world applications of convolutions?

Convolutions have a wide range of real-world applications, including image and speech recognition, natural language processing, medical imaging, and financial forecasting. They are also used in various fields of science, such as astronomy, biology, and physics, to analyze and interpret data.

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