One of the key benefits of the Fourier Transformation is it enables us to do **high-pass and low-pass filtering**.

After we apply the Fourier Transformation:



we need to create a filtering **mask**

### Low-Pass Filtering (Blurring) ###

A **low-pass filter removes high-frequency** components, which results in a blurred image. **Low-pass mask**:

### High-Pass Filtering (Edge Detection) ###

A **high-pass filter removes low-frequency** components and enhances edges. **High-pass mask**:


### Applying the filter ###

After creating the mask, we must apply a filter and transform our photo back to the spatial domain:


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.

Object detection is a fundamental task in computer vision that involves identifying and localizing objects within an image. Unlike image classification, which assigns a single label to an entire image, object detection not only classifies objects but also determines their positions using bounding boxes. This section covers key techniques and algorithms used in object detection, ranging from traditional methods to deep learning-based approaches like YOLO and U-Net.

Computer Vision Course Outline

Low-pass and High-pass Filters

Low-Pass Filtering (Blurring)

High-Pass Filtering (Edge Detection)

Applying the filter

Solution

Low-pass and High-pass Filters

Low-Pass Filtering (Blurring)

High-Pass Filtering (Edge Detection)

Applying the filter

Solution