This page is a part of, a wiki devoted to computer vision. It focuses on low level computer vision, digital image analysis, and applications. It is designed as an online textbook but the exposition is informal. It geared towards software developers, especially beginners, and CS students. The wiki contains mathematics, algorithms, code examples, source code, compiled software, and some discussion. If you have any questions or suggestions, please contact me directly.

Fourier transform

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Fourier transform numerically describes the Texture and the pattern in the picture. This information may be important for certain computer vision tasks.

Fourier transform breaks the image into a combination of "sinusoidal" images. Those are represented by sine functions of various periods. The collection of these periods captures the texture - larger periods for larger patterns. Instead of periods it is common to speak in terms of frequencies = reciprocals of the periods.

A couple of facts:

  • If there is no clear pattern in the image, the data produced by Fourier transform will tell you very little. For example, it'll tell you how "busy" the image is. (In this case, Fourier transform remains useful as an image processing tool.)
  • Fourier transform processes the image "globally" as a certain kind of averaging over the whole image. As a result, the data is highly affected by a partial image degradation.

To see the math of Fourier transform, go to [1] for now.

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