Foundations of Computer Vision: Algorithms and Physics

Computer vision is the core technology behind autonomous systems, medical imaging, and modern security. Understanding the underlying logic and physical principles of how images are formed and processed is essential for anyone looking to build reliable vision-based applications. This course provides a comprehensive introduction to the field, focusing on the fundamental algorithms that allow machines to perceive the world. You will transition from learning how light interacts with surfaces to implementing complex spatial recognition techniques. By the end of this course, you will have a solid grasp of the mathematical frameworks used to extract meaningful data from pixels. What you'll learn: - Understand the physics of light, image formation, and camera geometry - Apply mathematical filters for edge detection and feature extraction - Master the principles of camera calibration and spatial transformations - Practice developing algorithms for motion estimation and object tracking - Explore the basics of neural networks and deep learning for visual recognition - Analyze 3D reconstruction techniques and depth perception methods The course begins with essential terminology and the physics of optics before moving into algorithmic implementations and modern integration with machine learning. Designed for beginners with an interest in technology and math, this program requires no prior experience in vision systems. Start building your foundational knowledge of computer vision today.