This foundational computer vision lecture series provides a deep introduction to how visual information is formed, captured, and interpreted computationally. The course begins with organizational topics and an overview of the evolution and history of computer vision, helping learners understand how modern vision systems emerged. Students then explore the physics and mathematics behind image formation, including geometric transformations, photometric modeling, and the full image sensing pipeline that converts light into digital data. These principles form the backbone of camera modeling and visual perception algorithms. The series continues with structure-from-motion preliminaries, introducing how 3D structure can be inferred from multiple images — a key concept in robotics, augmented reality, and 3D reconstruction. Throughout the lectures, emphasis is placed on connecting theory with practical computer vision workflows, enabling learners to understand how raw sensor data becomes meaningful spatial information. By completing this series, students gain a rigorous conceptual framework for modern vision pipelines, preparing them for advanced study in computer vision, robotics, and AI-driven imaging systems. This course is ideal for learners seeking a strong theoretical foundation before moving into applied