Having covered basic image representation and processing operations that often affect the entire image, we now turn our attention to identifying specific, meaningful structures within images. These structures, known as features, represent distinctive points, lines, or regions that can serve as landmarks.

This chapter introduces the fundamental concepts of feature detection. You will learn:

• What constitutes a feature in an image, such as edges and corners.
• The motivation behind detecting these features for various computer vision tasks.
• Introductory methods for edge detection, including the Sobel operator for gradient calculation and the widely used Canny edge detector.
• The basic idea behind corner detection, focusing on the conceptual approach of the Harris detector.
• A brief introduction to the concept of feature descriptors, which characterize the detected features numerically.

We will cover the theory behind these methods and conclude with a hands-on exercise applying edge detection techniques using OpenCV. Understanding features is a step towards analyzing image content in more detail, preparing for tasks like object matching and recognition.