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This module lays the theoretical foundation of media computing. It covers the foundational topics that provide a general framework for solving high-level media computing problems.

This module focuses on the conceptual foundation common to various media computing topics. This common foundation is
It gives rise to more sophisticated concepts including fitting, transformations, registrations and structure discovery. These concepts are applied to the solving of high-level, structured media problems. For the purpose of illustration, this module will focus primarily on images, 3D models, temporal sequences of simple objects.

This module is targeted towards upper undergraduate and beginning graduate students.
If you are interested in building a good foundation for research or in solving high-level media computing problems in a thoughtful manner, then this module is for you.

For frequently asked questions, please refer to the FAQ page.
What this is not
This module is not about low-level processing at the levels of signal and features. So, it does not cover signal processing, codec, streaming, feature extraction, etc. It also does not cover classification, neural networks and deep learning, which have separate modules for them.

If you are interested in advanced, cutting-edge technologies, please consider taking a 6000-level module instead.

For frequently asked questions, please refer to the FAQ page.
After taking this module, students will learn:
1. Knowledge: Mathematical models and algorithms for fitting, transformations, registrations and structure recovery.
2. Application: Apply knowledge to media problem solving, i.e., problem formulation and algorithm design. Programming is optional.
3. Skill: Familiarity with python programming for numerical computation.

Basic linear algebra and calculus, with a little bit of probability and statistics.
Background regarding images and 3D graphics models are useful but not absolutely necessary.

Applied linear algebra is used extensively in this module.
You need to work with algorithms in the form of linear algebra a lot.

If you do not have sufficient math background, then this module is not suitable for you.

Please download Exercise 1 and Exercise 2 try them out.
If you cannot do these exercises by yourself, then this module is not suitable for you.
Assessment Individual
Assignments 50%  
Team Project 10% 30%
Participation 10%  
Total 70% 30%

Please refer to LumiNUS CS5240 for more details.
A/Prof. Leow Wee Kheng
Email: leowwk@comp.nus.edu.sg
Office: AS6 #05-07
10 August 2022