This Computer Vision Fundamentals – Image Formation & Signal Processing Course provides a structured introduction to the core principles of how images are formed, represented, and processed in computer vision systems. It is designed for beginners and intermediate learners who want to build a strong theoretical foundation before moving into advanced AI and deep learning topics.
The course begins with an introduction and overview of computer vision, explaining its scope, applications, and importance in modern technology. Learners will also explore the history of computer vision to understand how the field has evolved over time.
Students will then study image formation, learning how real-world scenes are captured and converted into digital images. This includes an explanation of camera models and basic imaging principles. The course continues with image representation, showing how visual data is stored and structured for computational processing.
A key part of the course focuses on linear filtering techniques used to enhance, smooth, or detect features in images. Learners will also explore the frequency domain representation of images, which is essential for advanced signal processing and image analysis techniques.
By the end of this course, learners will understand the foundational concepts of computer vision, including image formation, filtering, and frequency
