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COMP61342 Computer Vision syllabus 2017-2018

COMP61342 materials

COMP61342 Computer Vision

Level 6
Credits: 15
Enrolled students: 44

Course leader: Aphrodite Galata


Additional staff: view all staff

Additional requirements

  • Pre-requisites

    Basic knowledge of linear algebra, basic calculus, programming experience (C/C++ or Matlab programming)

Assessment methods

  • 50% Written exam
  • 50% Coursework
Timetable
SemesterEventLocationDayTimeGroup
Sem 2 P4 Lecture 2.15 Thu 09:00 - 17:00 -
Themes to which this unit belongs
  • Making Sense of Complex Data
  • Computer Science units for ACSwITM students (semester 2)

Overview

This unit will give students a foundation in the subject of machine 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 interpretting 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.

Emphasis will also be placed on the importance of understanding algorithmic stability and optimality as a framework for algorithmic design and research methodology.

This course unit is designed for students that are interested in Computer Vision, Artificial Intelligence, or Machine Learning. This course unit is also appropriate for students with an interest in Computer Graphics.

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.

Teaching methods

Lectures

1 day per week (5 weeks)

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

Employability skills

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

Learning outcomes

Programme outcomeUnit learning outcomesAssessment
G1Have an understanding of commmon machine vision algorithms.
  • Examination
G1 G2Have a knowledge of the statistical design of algorithms.
  • Examination
G1 G2Have a knowledge of the properties of image data and be able to solve problems about extraction of features and other quantitative information.
  • Examination
G2Be able to design basic systems for image analysis and evaluate and justify the design.
  • Lab assessment
  • Examination
G3Be 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.
  • Lab assessment
  • Examination
G4Be able to work effectively as a member of a group to prepare presenations describing complex machine vision algorithms to their peers.
  • Tutorial

Reading list

COMP61342 does not have a specified reading list.

Additional notes

Course unit materials

Links to course unit teaching materials can be found on the School of Computer Science website for current students.