Postgraduate Course: Advanced Vision (INFD11002)
|School||School of Informatics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Year 5 Undergraduate)
||Availability||Not available to visiting students
|Summary||*This course replaced Advanced Vision INFR11151 from 2019/20*
*This course is available to distance learning students within the School of Informatics and students on the Data Science, Technology and Innovation programme.*
The main aim of the course 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.
Course Delivery Information
|Academic year 2019/20, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Written Exam 70%, Coursework 30 %, Practical Exam 0%
There is one coursework assignment worth 30%.
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.
||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.
|Keywords||Advanced Vision,Distance Learning
|Course organiser||Dr Robert Fisher
Tel: (0131 6)51 3441
|Course secretary||Ms Lindsay Seal
Tel: (0131 6)50 2701