THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2017/2018

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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
Credit level (Normal year taken)SCQF Level 11 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis 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.
Course description 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. Students are also introduced to ethical issues that might arise when using image analysis technology.

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
Entry Requirements (not applicable to Visiting Students)
Pre-requisites It is RECOMMENDED that students have passed Introduction to Vision and Robotics (INFR09019)
Co-requisites
Prohibited Combinations 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.

Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2017/18, Available to all students (SV1) 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, Summative Assessment Hours 3, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 68 )
Assessment (Further Info) Written Exam 75 %, Coursework 25 %, Practical Exam 0 %
Additional Information (Assessment) There is one assignment worth 25%.

Any programming language can be used, but Matlab is the language used in the lecture materials.

You should expect to spend approximately 36 hours on the coursework for this course.
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. understand machine vision principles (assessed by exam)
  2. be able to acquire and process raw image data (assessed practical) and to relate image data to 3D scene structures (assessed practical)
  3. know the concepts behind and how to use several model-based object representations, and to critically compare them (assessed by exam)
  4. know many of the most popularly used current computer vision techniques (assessed by exam)
  5. undertake computer vision work in MATLAB (assessed practical)
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)
Additional Information
Course URL http://course.inf.ed.ac.uk/av
Graduate Attributes and Skills Not entered
KeywordsNot entered
Contacts
Course organiserDr Robert Fisher
Tel: (0131 6)50 3098
Email: R.B.Fisher@ed.ac.uk
Course secretaryMr Gregor Hall
Tel: (0131 6)50 5194
Email: gregor.hall@ed.ac.uk
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