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Course Detail

Course Name Computer Vision And Immersive Technologies
Course Code 25WN737
Program M.Tech. Wireless Networks & Applications (Specialising in IoT, AI, 5G, Blockchain) (For Working Professionals & Regular Students)
Credits 3
Campus Amritapuri

Syllabus

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. 

Objectives and Outcomes

Course Outcome Statement (CO) 

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 

Text Books / References

  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). 

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