Feature Detection and Description in Computer Vision

This post is about brief understanding of feature detection and description concepts in computer vision. In which we explore their definitions, common techniques, simple python implementations and some limitations.

What is feature detection and description?

Feature detection and description are fundamental concepts in computer vision that play a crucial role in various tasks such as image recognition, object tracking, and image stitching. These concepts enable computers to identify distinct and informative parts of an image, making it easier to understand and analyze visual data. Here’s an overview of these concepts:

Feature Detection: Feature detection involves identifying specific points, regions, or structures in an image that are significant and can be used as references for further analysis. These features are usually characterized by their uniqueness, repeatability, and robustness to variations such as lighting changes, rotations, and scale transformations. Common types of features detected include corners, edges, blobs, and keypoints.

Common Techniques for Feature Detection:

• Harris Corner Detection: Detects corners in an image by analyzing changes in intensity in different directions.