Postgraduate Course: Image and Vision Computing (UG) (INFR11251)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course follows the delivery and assessment of Image and Vision Computing (INFR11140) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11140 instead. |
| Course description |
This course follows the delivery and assessment of Image and Vision Computing (INFR11140) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11140 instead.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | Students MUST NOT also be taking
Image and Vision Computing (INFR11140) AND
Introduction to Vision and Robotics (INFR09019) AND
Image and Vision Computing (INFD11004)
|
Other requirements | This course follows the delivery and assessment of Image and Vision Computing (INFR11140) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11140 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
| Pre-requisites | As above. |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2023/24, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
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
44 )
|
| Assessment (Further Info) |
Written Exam
50 %,
Coursework
50 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
50% Exam
50% Coursework
The coursework component will include a practical mini-project to implement an actual computer vision application, and occasional quizzes and/or other regular activities to promote engagement (no more than 15% of total mark). |
| Feedback |
Students will receive formative feedback through online tutorial participation, eg. via Skype or Collaborate, and Learns online discussion forum. Each student will also receive formative feedback through intermediate stages of the development of the mini-project. 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. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | Image and Vision Computing (UG) (INFR11251) | 2:00 | |
Learning Outcomes
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
|
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. |
| Keywords | IVC,Computer Vision,Image Processing,Computer Graphics |
Contacts
| Course organiser | Dr Changjian Li
Tel:
Email: Changjian.li@ed.ac.uk |
Course secretary | Ms Lindsay Seal
Tel: (0131 6)50 2701
Email: lindsay.seal@ed.ac.uk |
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