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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Advanced Vision (Level 11) (INFR11031)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Course typeStandard AvailabilityNot available to visiting students
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) Credits10
Home subject areaInformatics Other subject areaNone
Course website Taught in Gaelic?No
Course descriptionThis 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)
Prohibited Combinations Students MUST NOT also be taking Advanced Vision (Level 10) (INFR10001)
Other requirements For Informatics PG and final year MInf students only, or by special permission of the School. 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
Course Delivery Information
Delivery period: 2011/12 Semester 2, Not available to visiting students (SS1) WebCT enabled:  No Quota:  None
Location Activity Description Weeks Monday Tuesday Wednesday Thursday Friday
No Classes have been defined for this Course
First Class Week 1, Monday, 14:00 - 14:50, Zone: Central. AT 2.14
Additional information M-F 0900-1700 as arranged.
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

There are 2 lab-based practicals at 10% each, plus a 3rd assignment for 10% more. The lab exercise is done in teams of 2. These exercises usually involve: 1) basic image processing and 2) 3D scene or video analysis. Any programming language can be used, but Matlab is the language used in the lecture materials. The 3rd assignment will be to create a web page summarising a topic in CVonline for which no material exists at present. There will be a small set of alternative topics suitable for level 11 students available for the web page construction.
Special Arrangements
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)
Study Abroad Not entered
Study Pattern Lectures 20
Tutorials 0
Timetabled Laboratories 0
Non-timetabled assessed assignments 36
Private Study/Other 44
Total 100
KeywordsNot entered
Course organiserDr Michael Rovatsos
Tel: (0131 6)51 3263
Course secretaryMiss Kate Weston
Tel: (0131 6)50 2701
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