MSc ACS: Computer Security
Year of entry: 2020
Course unit details:
|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|
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.
Basic knowledge of linear algebra, basic calculus, programming experience (C/C++ or Matlab programming)
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.
Have an understanding of common machine vision algorithms.
Have a knowledge of the statistical design of algorithms.
Have a knowledge of the properties of image data and be able to solve problems about extraction of features and other quantitative information.
Be able to design basic systems for image analysis and evaluate and justify the design.
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.
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
1 day per week (5 weeks)
- Analytical skills
- Group/team working
- Oral communication
- Written communication
|Written assignment (inc essay)||50%|
COMP61342 reading list can be found on the School of Computer Science website for current students.
|Independent study hours|
|Aphrodite Galata||Unit coordinator|
Course unit materials
Links to course unit teaching materials can be found on the School of Computer Science website for current students.