Catalogue of Modules, University of Nottingham

G53VIS Computer Vision
(Last Updated:08 September 2017)

Year  11/12

Total Credits: 10

Level: Level 3

Target Students:  Part II undergraduate students in the School of Computer Science. Also available to students from other Schools with the agreement of the module convenor.  Available to JYA/Erasmus students.

Taught Semesters:

Autumn Assessed by end of Autumn Semester 

Prerequisites: Or equivalent programming experience (eg G51PRG 2009/10). Background knowledge of vision and image processing an advantage, for example G52IIP

Corequisites:  None.

Summary of Content:  

This module is part of the Graphics and Vision theme in the School of Computer Science.

Building on G52IIP this module examines current techniques for the extraction of useful information about a physical situation from individual and sets of images. Particular emphasis is placed on the identification of objects, recovery of three-dimensional shape &motion, and the recognition of events. Topics covered include: advanced segmentation and feature extraction, motion computation and tracking, stereo vision and the use of hidden markov models in higher level analysis.

Method and Frequency of Class:

ActivityNumber Of WeeksNumber of sessionsDuration of a session
Lecture 11 weeks2 per week1 hour
Tutorial 11 weeks1 per week1 hour

Activities may take place every teaching week of the Semester or only in specified weeks. It is usually specified above if an activity only takes place in some weeks of a Semester

Further Activity Details:
Practical problems tackled in coursework assignments. Guided reading will also be used to cover some of the topics. Tutor led hours 22, student directed hours 25, assessment/revision 28.

Method of Assessment: 

Assessment TypeWeightRequirements
Exam 1 60 2 hr written examination 
Coursework 1 10 One-page project proposal 
Coursework 2 30 MATLAB project and 2,500 word report 

Professor T Pridmore

Education Aims:  To provide a grounding in current research areas of computer vision. To give experience in implementing computer vision algorithms.

Learning Outcomes:  Knowledge and Understanding: Experience in implementing image processing and vision algorithms. Understanding of current techniques in image processing and computer vision and an awareness of their limitations. An appreciation of the underlying mathematical principles of computer vision. Intellectual Skills: Apply knowledge of computer vision techniques to particular tasks. Evalutate and compare competing approaches to vision tasks. Professional Skills: Develop a working knowledge of image processing algorithms and libraries and evaluate the applicability of various algorithms and operators to particular tasks. Transferable Skills: Apply knowledge of the methods and approaches presented to problem domains use the available resources (libararies, internet, etc) to supplement the course material.

Offering School:  Computer Science

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