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.
|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
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:
|Activity||Number Of Weeks||Number of sessions||Duration of a session|
|Lecture||11 weeks||2 per week||1 hour|
|Tutorial||11 weeks||1 per week||1 hour|
Method of Assessment:
|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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