Postgraduate Course: Advanced Vision (Level 11) (INFR11031)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Available to all students |
| Credit level (Normal year taken) | SCQF Level 11 (Year 4 Undergraduate) |
Credits | 10 |
| Home subject area | Informatics |
Other subject area | None |
| Course website |
http://www.inf.ed.ac.uk/teaching/courses/av |
Taught in Gaelic? | No |
| 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 several vision systems during the course of the lecture series and practicals. |
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
It is RECOMMENDED that students have passed
Introduction to Vision and Robotics (INFR09019)
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Co-requisites | |
| Prohibited Combinations | Students MUST NOT also be taking
Advanced Vision (Level 10) (INFR10001)
|
Other requirements | This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.
This course assumes an ability to program in MATLAB and the following mathematical knowledge: Eigenvectors, Basic matrix algebra: multiply, inverse, Basic 3D geometry: rotations, translations, Covariance matrices, Principal Component Analysis, Basics of surfaces in 3D, Least Square Error estimation.
This course assumes an ability to program in MATLAB and the following mathematical knowledge: Eigenvectors, Basic matrix algebra: multiply, inverse, Basic 3D geometry: rotations, translations, Covariance matrices, Principal Component Analysis, Basics of surfaces in 3D, Least Square Error estimation. |
| Additional Costs | None |
Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
|
| Delivery period: 2014/15 Semester 2, Available to all students (SV1)
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Learn enabled: No |
Quota: None |
|
Web Timetable |
Web Timetable |
| Course Start Date |
12/01/2015 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 20,
Supervised Practical/Workshop/Studio Hours 10,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
66 )
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| Additional Notes |
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| Breakdown of Assessment Methods (Further Info) |
Written Exam
70 %,
Coursework
30 %,
Practical Exam
0 %
|
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | | 2:00 | |
Summary of Intended Learning Outcomes
1 - understand machine vision principles (assessed by exam).
2 - be able to acquire and process raw image data (assessed practical).
3 - be able to relate image data to 3D scene structures (assessed practical).
4 - know the concepts behind and how to use several model-based object representations, and to critically compare them (assessed by exam).
5 - know many of the most popularly used current computer vision techniques (assessed by exam).
6 - undertake computer vision work in MATLAB (assessed practical).
7 - be able to review and critique current research work (literature review). |
Assessment Information
Written Examination 70
Assessed Assignments 30
Oral Presentations 0
Assessment
There are 2 lab-based practicals at 15% each.
The lab exercises are done in teams of 2. These exercises usually involve:
1) basic 2D image and video processing and 2) 3D scene, data, or video analysis.
Any programming language can be used, but Matlab is the language used in the lecture materials.
|
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
In the course of constructing six vision systems, students will learn about: image noise reduction, region growing, boundary segmentation, Canny edge detector, Hough transform, RANSAC, 2D and 3D coordinate systems, interpretation tree matching, rigid 2D object modeling, 2D position estimation, point distribution models, 3D range sensors, range data segmentation, 3D position estimation, stereo sensors, motion tracking and various approaches to object recognition.
The activities of the module 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.
Relevant QAA Computing Curriculum Sections: Computer Vision and Image Processing |
| Transferable skills |
Not entered |
| Reading list |
* R. Jain, R. Kasturi, B. G. Schunck, Machine Vision, McGraw Hill International Editions, 1995
* T. Morris - "Computer Vision and Image Processing" (Palgrave, 1st Edition, 2004)
* E. Trucco and A. Verri, "Introductory Techniques for 3-D Computer Vision", Prentice Hall, 1998
* E.R. Davis - "Machine Vision - Theory, Algorithms and Practice" (Elsevier, 3rd Edition, 2005)
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| Study Abroad |
Not entered |
| Study Pattern |
Lectures 20
Tutorials 0
Timetabled Laboratories 0
Non-timetabled assessed assignments 36
Private Study/Other 44
Total 100 |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Mary Cryan
Tel: (0131 6)50 5153
Email: |
Course secretary | Miss Kate Farrow
Tel: (0131 6)50 2706
Email: |
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