MSc ACS: Computer Security

Year of entry: 2020

Course unit details:
Computer Vision

Unit code COMP61342
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 unit will give students a foundation in the subject of Computer Vision. This will involve gaining familiarity with algorithms for low-level and intermediate-level processing and considering the organisation of practical systems. Particular emphasis will be placed on the importance of representation in making explicit prior knowledge, control strategy and interpreting hypotheses.

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

  • 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

Lectures

1 day per week (5 weeks)

Employability skills

Analytical skills
Group/team working
Oral communication
Research
Written communication

Assessment methods

Method Weight
Written exam 50%
Written assignment (inc essay) 50%

Feedback methods

The assessment for this course unit is based on a combination of coursework and a closed-book exam. The coursework consists of: reports on a set of practical assignments carried out using MATLAB, an essay based on reading a collection of journal papers and a group presentation on selected research papers. Feedback will be provided via moodle and in person after the group presentations.

Recommended reading

COMP61342 reading list can be found on the School of Computer Science website for current students.

Study hours

Independent study hours
Independent study 68

Teaching staff

Staff member Role
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.

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