Computer Vision (G6032)
Computer Vision
Module G6032
Module details for 2021/22.
15 credits
FHEQ Level 5
Library
Course web pages and online teaching material
Sonka M., Hlavac V. & Boyle R. (1993) Image Processing, Analysis and Machine Vision. London: Chapman & Hall Computing.
Shapiro L.G. & Stockman G.C. (2001) Computer Vision. London etc.: Prentice Hall
Trucco E. & Verri A. (1998) Introductory Techniques for 3-D Computer Vision. Upper Saddle River NJ: Prentice Hall.
Gonzalez, R.C., Woods, R.E. & Eddins, S.L. (2004) Digital Image Processing Using Matlab. Upper Saddle River NJ: Pearson Prentice Hall.
Module Outline
This module introduces the field of computer vision, which enables computers to interpret image and video data. Topics include: convolutions and image processing; feature detection and description; object detection; segmentation; 3D geometry; deep convolutional neural networks. The module emphasises practical techniques and real-world applications. You will be introduced to a suitable software package.
Pre-Requisite
Ability to program in a procedural programming language.
Module learning outcomes
Write and document a computer program to extract useful information from image data.
Propose designs for simple computer vision systems.
Determine the applicability of a variety of computer vision techniques to practical problems.
Describe and recognise the effects of a variety of image processing operations.
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 100.00% | |
| Coursework components. Weighted as shown below. | ||
| Report | A2 Week 1 | 80.00% |
| Computer Based Exam | T2 Week 4 (1 hour) | 10.00% |
| Computer Based Exam | T2 Week 8 (1 hour) | 10.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Spring Semester | Lecture | 1 hour | 22222222222 |
| Spring Semester | Class | 1 hour | 11111111111 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
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