### 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**:


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.


This course provides a comprehensive introduction to Computer Vision, covering both fundamental and advanced topics. You'll learn how machines process, analyze, and interpret visual data through various techniques such as image processing, feature extraction, object detection, and deep learning models.

Computer Vision enables machines to interpret and analyze visual data, mimicking human perception. This section covers the basics of image representation, color models, and mathematical foundations essential for understanding how computers process images. You'll explore real-world applications, from autonomous vehicles to medical imaging, and learn how Computer Vision integrates with AI and machine learning. 

OpenCV is a powerful library for image manipulation and computer vision tasks. This section covers essential techniques like image filtering, transformations, edge detection, and segmentation. You'll learn how to perform blurring, thresholding, contour detection, and feature extraction to enhance and analyze images efficiently.

CNNs process visual data using convolution, pooling, and activation layers to extract features for tasks like image classification and object detection. Key components include padding, convolution for feature extraction, pooling for complexity reduction, and activation for non-linearity. Popular architectures like AlexNet, VGG, and ResNet power AI in healthcare, autonomy, and security.

Computer Vision Course Outline

Fourier Transform

The Fourier Transform

Solução

Fourier Transform

The Fourier Transform

Solução