
- UCAS course code
- I100
- UCAS institution code
- M20
MEng Computer Science with Industrial Experience / Course details
Year of entry: 2021
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Course unit details:
Computer Vision
Unit code | COMP61342 |
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Credit rating | 15 |
Unit level | FHEQ level 7 – master's degree or fourth year of an integrated master's degree |
Teaching period(s) | Semester 2 |
Offered by | Department of Computer Science |
Available as a free choice unit? | Yes |
Overview
This course unit treats vision as a process of inference from noisy and uncertain data and emphasizes probabilistic and statistical approaches. As such, it will also give students a foundation in the statistical methods of image analysis.
Topics covered in the course include perception of 3D scene structure from stereo; image filtering, smoothing, edge detection; segmentation and grouping; learning, recognition, and search; tracking and motion estimation; behaviour modelling.
This course unit is designed for students that are interested in Computer Vision, Artificial Intelligence, or Machine Learning. It is also appropriate for students with an interest in Computer Graphics.
Pre/co-requisites
Pre-requisites
Basic knowledge of linear algebra, basic calculus, programming experience (C/C++ or Matlab programming)
Aims
To introduce the basic concepts and algorithmic tools of computer vision.
To introduce the problems of building practical vision systems.
To explore the role of representation and inference.
To explore the statistical processes of image understanding and develop an understanding of advanced concepts and algorithms.
To discuss novel approaches to designing vision systems that learn.
To develop skills in evaluation of algorithms for the purposes of understanding research publications in this area.
Learning outcomes
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Have an understanding of common machine vision algorithms.
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Have a knowledge of the statistical design of algorithms.
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Have a knowledge of the properties of image data and be able to solve problems about extraction of features and other quantitative information.
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Be able to design basic systems for image analysis and evaluate and justify the design.
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Be able to write a program for the analysis of image data and prepare a technical report on the evaluation of this program on suitable test data.
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Be able to work effectively as a member of a group to prepare presenations describing complex machine vision algorithms to their peers.
Teaching and learning methods
Lectures
1 day per week (5 weeks)
Employability skills
- Analytical skills
- Group/team working
- Oral communication
- Research
- Written communication
Assessment methods
Method | Weight |
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Written exam | 50% |
Written assignment (inc essay) | 50% |
Feedback methods
Recommended reading
COMP61342 reading list can be found on the Department of Computer Science website for current students.
Study hours
Independent study hours | |
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Independent study | 68 |
Teaching staff
Staff member | Role |
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Aphrodite Galata | Unit coordinator |
Additional notes
Course unit materials
Links to course unit teaching materials can be found on the School of Computer Science website for current students.