This Machine Vision course provides an advanced theoretical foundation in computer vision, focusing on how machines interpret motion, shape, and lighting from images. It bridges geometry, physics, and computational models used in modern vision systems.

You will start with an introduction to machine vision and core imaging concepts such as perspective projection and image formation. The course then explores motion analysis, including optical flow, constant brightness assumption, and closed-form solutions for motion estimation.

Next, you will study camera geometry concepts such as vanishing points and their role in camera calibration. These principles are essential for understanding how 3D scenes are reconstructed from 2D images.

The course also covers advanced topics in gradient space, reflectance maps, and image irradiance equations, which describe how light interacts with surfaces. You will explore shading models and special cases such as lunar surface imaging and electron microscopy.

A major part of the course focuses on shape from shading, including nonlinear equations, iterative solutions, and mathematical methods for reconstructing 3D shapes from lighting information.

Finally, you will learn edge detection techniques, subpixel accuracy, and line detection algorithms used in real-world vision systems.

By the end of this course, you will have a strong mathematical and physical understanding of machine vision systems and their applications in robotics and AI.