Module 19339 (2012)
Syllabus page 2012/2013
06-19339
Computational Vision
Level 2/I
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus
The Module Description is a strict subset of this Syllabus Page. (The University module description has not yet been checked against the School's.)
Relevant Links
Outline
The module provides an introduction to computer vision, intended for students with some prior background in AI. Appropriate computational models, techniques and algorithms will be introduced, so that students can both understand the relevant literature and construct simple sofware systems.
Aims
The aims of this module are to:
- provide a general introduction to computer vision
- give an overview of computational models of visual processing in animals
- introduce a number of different frameworks and representations for vision
- familiarise the student with a number of techniques and algorithms in computer vision
- provide a foundation for further study in the area of computer vision
Learning Outcomes
| On successful completion of this module, the student should be able to: | Assessed by: | |
| 1 | make informed choices about which sort of algorithms to apply to solve specific problems | Examination |
| 2 | use standard vision libraries or software to construct working vision systems | Practical work, Examination |
| 3 | apply algorithms to simplified problems by hand | Examination |
| 4 | discuss the advantages and drawbacks of different methods, explaining their working | Practical work, Examination |
Restrictions, Prerequisites and Corequisites
Restrictions:
None
Prerequisites:
None
Co-requisites:
None
Teaching
Teaching Methods:
2 hrs lectures per week, 1 hr lab/exercise class
Contact Hours:
Assessment
- Sessional: 1.5 hr examination (70%), continuous assessment (30%).
- Supplementary (where allowed): By examination only.
- The continuous assessment consists of a team project.
Recommended Books
| Title | Author(s) | Publisher, Date |
| Computer Vision | Dana Ballard & Chris Brown | Prentice Hall, 1982 |
| Computer Vision: A Modern Approach | David Forsyth & Jean Ponce | Prentice Hall, 2003 |
| Algorithms for Image Processing and Computer Vision | J R Parker | Wiley, 1996 |
Detailed Syllabus
-
Taught topics include (but are not limited to):
- Introduction to Computational and Human Vision
- Human Vision
- Edge Detection
- Noise Filtering
- Hough Transform
- ROC Analysis
- Motion
- Feature Detection
- Cross Correlation
- Face Recognition
- Objecct Recognition
- Laboratory Excercises:
- Matlab Tutorial
- Edge Detection
- Noise Filtering
- Hough Transform
- Eigenfaces
Last updated: 7 Jul 2009
Source file: /internal/modules/COMSCI/2012/xml/19339.xml
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus