This Computer Vision Fundamentals Course provides a structured introduction to the core principles behind computer vision systems. It is designed for students, researchers, and developers who want to understand how machines interpret and process visual information.
The course begins with an introduction to computer vision, including its organization, scope, and historical development. Learners will understand how the field evolved and how early research shaped modern AI-based vision systems.
Students will then explore image formation fundamentals, starting with basic primitives and transformations that describe how images are represented mathematically. This section explains how objects in the real world are converted into digital images.
The course continues with geometric image formation, covering how camera systems capture spatial relationships and project 3D scenes into 2D images. Learners will also study photometric image formation, which explains how light, color, and intensity influence image capture and perception.
By the end of this course, learners will have a strong understanding of the foundational principles of computer vision, including geometry, photometry, and image formation. This knowledge serves as a base for more advanced topics such as deep l
