Postgraduate Course: Advanced Vision (Level 11) (INFR11031)
|School||School of Informatics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Year 4 Undergraduate)
||Availability||Available to all students
|Summary||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.
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)
|| It is RECOMMENDED that students have passed
Introduction to Vision and Robotics (INFR09019) OR
Image and Vision Computing (INFR11140) OR
Robotics: Science and Systems (INFR11092)
||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
|High Demand Course?
Course Delivery Information
|Not being delivered|
On completion of this course, the student will be able to:
- understand machine vision principles (assessed by exam)
- be able to acquire and process raw image data (assessed practical) and to 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)
- undertake computer vision work in MATLAB (assessed practical)
|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)
|Course organiser||Dr Robert Fisher
Tel: (0131 6)51 3441
|Course secretary||Miss Clara Fraser
Tel: (0131 6)51 4164