Postgraduate Course: Advanced Vision (INFR11127)
|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
|Summary||Only available to students of the Data Science, Technology and Innovation (DSTI) online distance learning programme.
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.
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.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| Only available to students of the Data Science, Technology and Innovation (DSTI) online distance learning programme
|Additional Costs|| None. Students may wish to buy a MATLAB student license for their PC (£55)
Course Delivery Information
|Academic year 2017/18, Not available to visiting students (SS1)
|Course Start Date
|Learning and Teaching activities (Further Info)
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
|Assessment (Further Info)
|Additional Information (Assessment)
||Written Exam 75%, Coursework 25 %, Practical Exam 0 %
||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.
||Hours & Minutes
|Main Exam Diet S2 (April/May)||2:00|
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).
|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)
|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.
||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
|Course organiser||Dr Robert Fisher
Tel: (0131 6)50 3098
|Course secretary||Mrs Victoria Swann
Tel: (0131 6)51 7607