Course

Unit 1

Foundations of Computer Vision 

Image representation, color spaces (RGB, grayscale), image filtering, edge detection, feature detection (Harris, SIFT, ORB), feature descriptors and matching, geometric transformations, homography, optical flow, and motion estimation. 

Unit 2

3D Vision and Camera Pose Estimation 

Stereo vision, depth estimation, structure from motion, epipolar geometry, camera calibration, projection matrices, Perspective-n-Point (PnP) problem, pose estimation, and introduction to SLAM. 

Unit 3

Immersive Technologies: AR, VR, and MR 

AR systems and classifications (marker-based, markerless), tracking and registration, AR toolkits (ARToolkit, Vuforia), rendering and interaction; VR and MR concepts, hardware components, spatial mapping, ARCore, ARKit, MRTK, and immersive interaction design. 

Learning Objectives 

LO1: Demonstrate understanding of key computer vision techniques and explain their relevance in immersive technologies. 

LO2: Design and develop basic interactive AR/MR experiences using industry-standard platforms such as Unity, Vuforia, ARCore, or MRTK. 

Course Outcomes 

CO1. Understand the fundamentals of computer vision and its role in immersive systems. 

CO2. Apply image processing and vision-based tracking methods. 

CO3. Analyse the core principles and technologies behind AR, VR, and MR. 

CO4. Build basic AR/MR applications using industry tools and platforms. 

1. Szeliski, Richard. Computer vision: algorithms and applications. Springer Nature, 2022. 
2. Howse, Joseph, and Joe Minichino. Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms for computer vision and machine learning. Packt Publishing Ltd, 2020. 
3. Schmalstieg, Dieter, and Tobias Hollerer. Augmented reality: principles and practice. Addison-Wesley Professional, 2016. 
4. Craig, Alan B. “Understanding augmented reality: Concepts and applications.” (2013)