Postgraduate Course: Advanced Vision (INFR11127)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | The main aim is to give students who already have had an introduction to images and image processing a deeper understanding of the main concepts in 2D image, 3D image and video data processing. |
| Course description |
This module aims to build on the introductory computer vision material taught in Introduction to Vision and Robotics. The main aim is to give students an understanding of main concepts in visual processing by constructing or analysing several vision systems during the course of the lecture series and practicals. The 6 systems are for: rigid 2D part recognition, deformable 2D part recognition, rigid 3D part recognition from stereo data, rigid 3D part recognition from range sensing, target detection and tracking in video, and video based behaviour classification.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | For distance learning students only. |
| Additional Costs | None. Students may wish to buy a MATLAB student license for their PC (£55)
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Course Delivery Information
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| Academic year 2015/16, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 19,
Supervised Practical/Workshop/Studio Hours 7,
Feedback/Feedforward Hours 1,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
71 )
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| Assessment (Further Info) |
Written Exam
70 %,
Coursework
30 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Written Exam 70 %, Coursework 30 %, Practical Exam 0 % |
| Feedback |
Students will get formative feedback from the course tutors while doing their coursework and summative feedback from their marked practicals, their exams and from live feedback during their coursework demonstrations. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
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| Main Exam Diet S2 (April/May) | | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand machine vision principles (assessed by exam).
- Acquire and process raw image data (assessed practical).
- Relate image data to 3D scene structures (assessed practical).
- Know the concepts behind and how to use several model-based object representations, and to critically compare them (assessed by exam).
- Know many of the most popularly used current computer vision techniques (assessed by exam).
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Reading List
E.R. Davies, Machine Vision - Theory, Algorithms and Practice" (Elsevier, 3rd Edition, 2005) - (Content for about 1/2 the course)
Solomon & Breckon, Fundamentals of Digital Image Processing - A Practical Approach with Examples in Matlab", Wiley-Blackwell, 2010, ISBN: 978-0470844731 (content for about 1/2 of course)
R. Szeliski, "Computer Vision", Springer, 2011, ISBN: 978-1-84882-934-3 (Content for about 1/2 of course)
T. Morris, "Computer Vision and Image Processing" (Palgrave, 1st Edition, 2004)
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Additional Information
| Graduate Attributes and Skills |
The activities of the course are designed to further develop intellectual skills in the areas of: laboratory, writing (lab reports and short essays), teamwork, critical analysis, programming and laboratory skills. |
| Special Arrangements |
Students will need to have high-speed internet access suitable for downloading and watching video content, and access to matlab (from a local license or purchase of a student license from Matlab) for the coursework. |
| Keywords | Computer vision,image processing,artificial intelligence |
Contacts
| Course organiser | Dr Robert Fisher
Tel: (0131 6)50 3098
Email: R.B.Fisher@ed.ac.uk |
Course secretary | Ms Sarah Larios
Tel: (0131 6)51 4164
Email: sarah.larios@ed.ac.uk |
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