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Leer Fourier Transform | Image Processing with OpenCV
Computer Vision Course Outline
course content

Cursusinhoud

Computer Vision Course Outline

Computer Vision Course Outline

1. Introduction to Computer Vision
2. Image Processing with OpenCV
3. Convolutional Neural Networks
4. Object Detection
5. Advanced Topics Overview

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Fourier Transform

The Fourier Transform

The Fourier Transform (FT) is a fundamental mathematical tool used in image processing to analyze the frequency components of an image. It allows us to transform an image from the spatial domain (where pixel values are represented directly) to the frequency domain (where we analyze patterns and structures based on their frequency). This is useful for tasks like image filtering, edge detection, and noise reduction.

First, we need to convert the image to grayscale:

To compute the 2D Fourier Transform:

Here, fft2() converts the image from the spatial domain to the frequency domain, and fftshift() moves low-frequency components to the center.

To visualize the magnitude spectrum:

Since Fourier Transform outputs complex numbers, we take the absolute values (np.abs()) for a meaningful visualization.

The np.log function enhances visibility, as raw magnitude values vary greatly in scale.

Taak

Swipe to start coding

  • Apply Fourier Transform to image;
  • Calculate a magnitude spectrum.

Oplossing

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Was alles duidelijk?

Hoe kunnen we het verbeteren?

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Sectie 2. Hoofdstuk 2
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book
Fourier Transform

The Fourier Transform

The Fourier Transform (FT) is a fundamental mathematical tool used in image processing to analyze the frequency components of an image. It allows us to transform an image from the spatial domain (where pixel values are represented directly) to the frequency domain (where we analyze patterns and structures based on their frequency). This is useful for tasks like image filtering, edge detection, and noise reduction.

First, we need to convert the image to grayscale:

To compute the 2D Fourier Transform:

Here, fft2() converts the image from the spatial domain to the frequency domain, and fftshift() moves low-frequency components to the center.

To visualize the magnitude spectrum:

Since Fourier Transform outputs complex numbers, we take the absolute values (np.abs()) for a meaningful visualization.

The np.log function enhances visibility, as raw magnitude values vary greatly in scale.

Taak

Swipe to start coding

  • Apply Fourier Transform to image;
  • Calculate a magnitude spectrum.

Oplossing

Switch to desktopSchakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties
Was alles duidelijk?

Hoe kunnen we het verbeteren?

Bedankt voor je feedback!

Sectie 2. Hoofdstuk 2
Switch to desktopSchakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties
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