Durham University
Programme and Module Handbook

Undergraduate Programme and Module Handbook 2023-2024 (archived)


Department: Computer Science


Type Open Level 4 Credits 10 Availability Available in 2023/24 Module Cap None. Location Durham


  • COMP3527 Computer Vision


  • None

Excluded Combination of Modules

  • None


  • To enable students to critically evaluate the development of contemporary computer vision systems utilising both existing and emerging technologies.
  • To enable students to study and research a number of topic themes across relevant computer vision application areas, focusing on case studies, and undertake research within these topic themes.


  • Themes will be chosen from contemporary areas of computer vision including the following:
  • Advanced fundamental topics: segmentation, superpixels, saliency, optic flow and image registration in 2D/3D.
  • Computer vision for advanced visual semantic models, attribute learning, zero-shot learning, visual question and answering.
  • Computer vision for advanced object and scene understanding.
  • Computer vision for behaviour understanding.
  • Computer vision for security and biometrics.
  • Computer vision for image manipulation and augmentation.

Learning Outcomes

Subject-specific Knowledge:
  • On completion of this module, students will be able to demonstrate a systematic understanding of the contemporary computer vision topics presented and a critical awareness of how they are applicable to both current and emerging needs within the associated industrial and research environment.
Subject-specific Skills:
  • On completion of the module, students will be able to demonstrate:
  • an ability to critically analyse the task suitability of current and future approaches within each of the contemporary computer vision topics presented.
  • an ability to independently evaluate research issues within each contemporary computer vision topic grouping including state-of-the-art and common industrial applications thereof.
  • an ability to identify challenges and barriers to emerging issues within the contemporary computer vision topic areas and propose potential solutions.
  • an ability to propose, plan and carry out research focused on such contemporary computer vision approaches to support current and future software applications.
Key Skills:
  • On completion of the module, students will be able to demonstrate:
  • an ability to exercise judgement on current research topics.
  • an ability to propose and apply the appropriate techniques to a range of industrial and research applications.
  • an ability to effectively evaluate and communicate technical information at the forefront of the associated field.
  • an ability to confidently use relevant research material in the development of existing and new application areas.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

  • Lectures enable the students to learn new material relevant to logic, discrete structures and mathematics, as well as their applications.
  • Practical classes enable the students to put into practice learning from lectures and strengthen their understanding through application.
  • Formative and summative assessments assess the application of methods and techniques.

Teaching Methods and Learning Hours

Activity Number Frequency Duration Total/Hours
lectures 20 2 per week 1 hour 20
practical classes 2 2 set within the teaching period of the module 1 hour 2
preparation and reading 78
total 100

Summative Assessment

Component: Coursework Component Weighting: 100%
Element Length / duration Element Weighting Resit Opportunity
Summative Assignment 100% No

Formative Assessment:

Example formative exercises are given during the course.

Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University