Total Credits: 10
Level: Level 4
Target Students: Specialist MSc and Part III undergraduate students in the School of Computer Science. Also available to Part II undergraduate students in the School of Computer Science subject to Part I performance. Also available to students from other Schools with the agreement of the module convenor. Available to JYA/Erasmus students.
Availability: Not Available in the 2017/18 academic session.
|Spring||Assessed by end of Spring Semester|
Prerequisites: Or equivalent programming (eg G51PRG up to 2009/10) and image processing experience. Background knowledge of computer vision an advantage, for example G53VIS
|G52IIP||Introduction to Image Processing|
Summary of Content:
This module is part of the Graphics and Vision theme in the School of Computer Science.
This module examines and provides practical experience of the design, construction and evaluation of computer vision systems. Computer vision finds application in a wide variety of domains, including interactive systems, media and games, robotics and automation, document processing, surveillance and the natural sciences. This module emphasizes practical work, with students constructing a real application in one of these areas. Topics covered include: current applications, frameworks and toolkits, vision system requirements and limitations, evaluation tools and methodologies.
Method and Frequency of Class:
|Activity||Number Of Weeks||Number of sessions||Duration of a session|
|Lecture||11 weeks||1 per week||1 hour|
|Tutorial||11 weeks||2 per week||1 hour|
Method of Assessment:
|Coursework 1||30||Written report (2,500 words) on proposed design of vision system|
|Coursework 2||20||15 minute Presentation and Demo of final system|
|Coursework 3||50||Written report (5,000 words) on final system, including evaluation|
Professor T Pridmore
Dr P Blanchfield
Education Aims: To provide grounding in current research areas of computer vision. To give experience in implementing computer vision algorithms.
Knowledge and Understanding:
Understanding of the strengths, weaknesses and applicability of current techniques in image processing and computer vision.
An appreciation of the range of current and likely future applications of computer vision.
Experience in designing and implementing computer vision systems.
Apply knowledge of computer vision techniques to particular tasks.
Evalutate and compare competing approaches to vision tasks.
Evaluate vision systems.
Develop a working knowledge of image processing algorithms and libraries and evaluate the applicability of various algorithms and operators to particular tasks.
Apply knowledge of the methods and approaches presented to problem domains use the available resources (libraries, internet, etc) to supplement the course material.
Offering School: Computer Science
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