| Module Code | CS7GV1 |
| Module Name | Computer Vision |
| ECTS Weighting [1] | 5 ECTS |
| Semester Taught | Semester 1 |
| Module Coordinator/s | Subrahmanyam Murala |
Module Learning Outcomes
On successful completion of this module, students will be able to:
- Explain Perception, describe low to high level features;
- Understand and apply the mathematics involved in Computer Vision;
- Use programming languages (e.g. Matlab/Octave, Python) for developing applications with Computer Vision libraries and deep learning;
- Design Computer Vision modules as part of Intelligent Systems;
- Discuss critically (with a report, a demo and a presentation) a Computer Vision project.
Module Content
- Image processing, feature detection and matching, image registration, recognition and segmentation;
- Motion flow and object tracking in video;
- Mathematics for Computer Vision.
Teaching and Learning Methods
Lectures, laboratories (programming) and online (e.g. discussion board).
Assessment Details
| Assessment Component | Brief Description | Learning Outcomes Addressed | % of Total | Week Set | Week Due |
| A | Computer Vision without deep learning | LO2/LO3/LO4 | 40% | Week 3 | Week 8 |
| B | Computer Vision with deep learning | L01/2/4/5 | 60% | Week 8 | TBD |
Reassessment Details
Coursework, 100%.
Contact Hours and Indicative Student Workload
| Contact Hours (scheduled hours per student over full module), broken down by: | 22 hours |
| Lecture | 22 hours |
| Independent Study (outside scheduled contact hours), broken down by: | 94 hours |
| Preparation for classes and review of material (including preparation for examination, if applicable) | 42 hours |
| Completion of assessments (including examination, if applicable) | 52 hours |
| Total Hours | 116 hours |
Recommended Reading List
- Computer Vision: Algorithms and Applications, Richard Szeliski, 2021.
- Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition with a Youtube playlist.
- Computer Vision: Models, Learning, and Inference , Simon J.D. Prince, Cambridge University Press 2012.
- Computer Vision: A Modern Approach, Forsyth and Ponce, Pearson 2012 (available in TCD library).
- Programming Computer Vision with Python, Jan Erik Solem.
- Deep Learning, I. Goodfellow et al., MIT 2016 press.
Module Pre-requisites
Prerequisite modules: Mathematics (e.g. Linear algebra, Optimisation), statistics, machine learning, programming (e.g. Python), signal processing.
Other/alternative non-module prerequisites: Some computer programming experience.
Module Co-requisites
N/A