Timetable information in the Course Catalogue may be subject to change.

University Homepage
DRPS Homepage
DRPS Search
DRPS Contact
DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Image and Vision Computing (INFR11140)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryIn this course we will learn how images are formed given the objects in the three dimensional world, and the basics of how computer vision inverts this process - computing properties of the world from digital images. We will cover topics including basic image formation, image processing, detection, matching and recognition that allow computers to understand the world based on image content.
Course description The course proceeds in five parts from foundational concepts such as image formation, through to low-level image processing operations, before building upon those to develop image representations, and use those representations for higher-level tasks such as recognition and detection. The following four parts are roughly two weeks each.

Image formation. The basic mathematics and physics of how images are formed based on light reflected by real-world objects. Includes ideal pinhole camera and lens models. Some basic 3D geometry, radiometry and photometry.

Low-level image analysis. We will introduce basic algorithms such as convolution and filtering for image processing, and RANSAC for fitting. These will be applied for tasks such as edge detection, and line-fitting. To provide a taste of recognition students will perform shape recognition using Bayes theorem.

Image Representations: To support working with more unconstrained realistic images, we next introduce feature representations for both local and global features including colour histograms, HOG/SIFT, and descriptor bag of words.

High-level image analysis: Building upon these image representations, we discuss the topical tasks of object recognition and sliding window-based object detection.

Applications: Finally, we finish up with an introduction to some applications including basic video processing (optical flow), and foreground detection.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Students MUST NOT also be taking Introduction to Vision and Robotics (INFR09019) AND Image and Vision Computing (INFD11004) AND Image and Vision Computing (UG) (INFR11251)
Other requirements MSc students must register for this course, while Undergraduate students must register for INFR11251 instead.

Students should be comfortable with probability (Bayes theorem), linear algebra, and multivariate calculus. Students should know or be willing to learn Matlab programming for labs and coursework.
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Not being delivered
Learning Outcomes
On completion of this course, the student will be able to:
  1. explain the basic physics and mathematical principles of image formation
  2. understand basic image processing operations such as convolution
  3. write programs to solve basic image analysis tasks such as edge detection and line fitting
  4. understand the concepts of local and global image descriptors, and descriptor matching
  5. write programs to perform image analysis tasks of recognition and detection
Reading List
Relevant Books:
- Simon Prince, Computer Vision Models, CUP.
- Richard Szeliski, Computer Vision Algorithms & Applications, Springer.
- Forsyth & Ponce, Computer Vision a Modern Approach, Pearson.
Additional Information
Graduate Attributes and Skills The activities in this course will develop skills in lab work, report writing, and programming.

Team working skills. For group (probably in pairs) participation in the course mini-project.

Also the flipped classroom discussion sessions (see following section) will promote SCQF11 skills such as;
-Develop original and creative responses to problems and issues
-Critically review, consolidate and extend knowledge, skills, practices
-Thinking in a subject/discipline/sector.
KeywordsIVC,Computer vision,Image processing,Computer graphics
Course organiserDr Changjian Li
Course secretaryMs Lindsay Seal
Tel: (0131 6)50 2701
Help & Information
Search DPTs and Courses
Degree Programmes
Browse DPTs
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Combined Course Timetable
Important Information