This Computer Vision Fundamentals course introduces the core principles behind how images are formed and processed in modern vision systems. The course focuses on the mathematical and physical foundations of computer vision, making it ideal for learners who want a strong understanding of imaging and camera geometry.

The course begins with an introduction to image formation and the pinhole camera model, one of the most important concepts in computer vision. Learners study how cameras capture light and generate images through perspective projection in both 2D and 3D environments.

As the lectures progress, the course explains geometric projection models and how spatial information is transformed into digital images. Students learn about perspective projection variations and generalized camera projection systems used in visual computing and robotics.

The course also introduces lens systems, including thin lens models, depth of focus, and image exposure. Learners explore how focus, lighting, and optics influence image quality and computer vision performance.

Additional lectures cover pixels and color filter arrays, explaining how digital sensors represent color information in cameras and imaging devices.

By the end of the course, learners will understand the fundamental principles of camera imaging, optics, perspective geometry, and image sensing used in computer vision, robotics, photography, augmented reality, and AI vision systems.