Postgraduate Course: Image and Vision Computing (INFR11140)
|School||School of Informatics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|Summary||In 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.
Learning Experience: The course will be delivered in a flipped format, with students watching recorded lectures and reading material, with the lecture functioning as a discussion session.
Content: 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 color 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 introduction to some applications including basic video processing (optical flow), and foreground detection.
Entry Requirements (not applicable to Visiting Students)
|Prohibited Combinations|| Students MUST NOT also be taking
Introduction to Vision and Robotics (INFR09019)
Students MUST NOT also be taking
Image and Vision Computing (INFR11155)
|Other requirements|| 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?
Course Delivery Information
|Academic year 2019/20, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 20,
Seminar/Tutorial Hours 10,
Supervised Practical/Workshop/Studio Hours 20,
Feedback/Feedforward Hours 2,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Written Examination: 70%
Practical Examination: 0%
The course will contain both a practical mini-project (25%) to implement an actual computer vision application, weekly quiz in the flipped classroom session to promote engagement (5%) and exam (70%).
||Students will receive formative feedback through online tutorial participation, eg. via Skype or Collaborate, and Learn's online discussion forum. Each student will also receive formative feedback through intermediate stages of the development of the miniproject. Summative feedback will occur through written feedback on their project report and demonstration. Additionally, we will monitor class issues through the use of a class student representative, and also occasional SurveyMonkey polls.
||Hours & Minutes
|Main Exam Diet S1 (December)||2:00|
On completion of this course, the student will be able to:
- Explain the basic physics and mathematical principles of image formation.
- Understand basic image processing operations such as convolution.
- Write programs to solve basic image analysis tasks such as edge detection and line fitting.
- Understand the concepts of local and global image descriptors, and descriptor matching.
- Write programs to perform image analysis tasks of recognition and detection.
|Relevant Books: |
- Simon Prince, Computer Vision Models, CUP.
- Richard Szeliski, Computer Vision Algorithms & Applications, Springer.
- Forsyth & Ponce, Computer Vision a Modern Approach, Pearson.
|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 miniproject.
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.
|Keywords||IVC,Computer vision,Image processing,Computer graphics
|Course organiser||Ms Laura Sevilla-Lara
|Course secretary||Ms Lindsay Seal
Tel: (0131 6)50 2701