Kursusindhold

Computer Vision Course Outline

1. Introduction to Computer Vision

What is Computer Vision?Fundamentals of Image Processing Linear algebra for image manipulation

2. Image Processing with OpenCV

Basic Transformations Fourier Transform Low-pass and High-pass Filters Noise Reduction and Smoothing Histogram Equalization Super-Resolution Techniques Edge Detection Corner and Blob Detection

3. Convolutional Neural Networks

Introduction to Convolutional Neural Networks Convolution Layers Pooling Layers Flattening Activation Functions Overview of Popular CNN Models Challenge: Building a CNN

4. Object Detection

Object Localization Object Detection Bounding Box Predictions Intersection Over Union (IoU) and Evaluation Metrics Non-Max Suppression (NMS)Anchor Boxes YOLO Model Overview Challenge: Object Detection with Custom Model and YOLO

5. Advanced Topics Overview

Transfer Learning in Computer Vision Overview of Face Recognition Overview of Image Generation

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.

Opgave

Swipe to start coding

Apply Fourier Transform to image;
Calculate a magnitude spectrum.

Løsning

Skift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder

Var alt klart?

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Sektion 2. Kapitel 2

Fourier Transform

The Fourier Transform

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.

Opgave

Swipe to start coding

Apply Fourier Transform to image;
Calculate a magnitude spectrum.

Løsning

Skift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder

Var alt klart?

Tak for dine kommentarer!

Sektion 2. Kapitel 2

Skift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder