Advanced Computer Science: Multi-Core Computing [MSc]

Computer Vision


Unit code: COMP61342
Credit Rating: 15
Unit level: Level 6
Teaching period(s): Semester 2
Offered by School of Computer Science
Available as a free choice unit?: Y

Requisites

None

Additional Requirements

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.

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.

Teaching and learning methods

Lectures

1 day per week (5 weeks)

Learning outcomes

Learning outcomes are detailed on the COMP61342 course unit syllabus page on the School of Computer Science's website for current students.

Employability skills

  • Analytical skills
  • Group/team working
  • Oral communication
  • Research
  • Written communication

Assessment methods

  • Written exam - 50%
  • Written assignment (inc essay) - 50%

Recommended reading

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

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.

Study hours

  • Independent study hours - 68 hours

Teaching staff

Aphrodite Galata - Unit coordinator

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