This Machine Vision & Optical Flow Course introduces the fundamental principles of how machines interpret motion and visual changes in dynamic environments. It is designed for students and engineers interested in computer vision, robotics, and motion analysis systems.
The course begins with an introduction to machine vision, explaining how visual systems process images over time rather than as static frames. Learners will study image formation and perspective projection, including how motion affects the way scenes are captured by cameras.
Students will then explore motion fields and time derivatives, which describe how objects move within an image sequence. The course explains time-to-contact (TTC) and focus of expansion (FOE), which are key concepts used in navigation, robotics, and autonomous systems.
A major part of the course focuses on optical flow techniques, including fixed optical flow, constant brightness assumption, and closed-form solutions for estimating motion. Learners will understand how motion is computed from changes in pixel intensity across frames.
The course also covers real-world applications such as optical mouse systems, vanishing points, and camera calibration techniques used in advanced vision systems.
By the end of this course, learners will understand how motion is analyzed in machine vision systems and how optical flow and geometric
