Postgraduate Course: Advanced Vision (Level 11) (INFR11031)
Course Outline
| 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 |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This module aims to build on the introductory computer vision material taught in Image and Vision Computing. The main aim is to give students an understanding of the main concepts in computer vision during the course of the lecture series and practicals. |
| Course description |
Students will learn about: object detection and segmentation, video understanding (action classification, optical flow, etc), vision and language, learning from limited data (few-shot learning, weakly supervised data, etc) and reconstruction.
The activities of the module are designed to further develop intellectual skills in the areas of: laboratory, writing (lab reports), teamwork, critical analysis, programming and laboratory skills.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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)
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Co-requisites | |
| Prohibited Combinations | |
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.
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Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2021/22, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
| Course Start Date |
17/01/2022 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 19,
Supervised Practical/Workshop/Studio Hours 7,
Feedback/Feedforward Hours 1,
Summative Assessment Hours 3,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
68 )
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| Assessment (Further Info) |
Written Exam
50 %,
Coursework
50 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
There is one coursework assignment worth 50%. The exam is worth 50%. |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- understand machine vision principles
- know many of the most popularly used current computer vision techniques
- be able to process and infer concepts from image data
- know the concepts behind and how to use several computer vision methods, and to critically compare them
- undertake computer vision programming work
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Reading List
| Aaron Courville, Ian Goodfellow, and Yoshua Bengio, Deep Learning |
Contacts
| Course organiser | Ms Laura Sevilla-Lara
Tel:
Email: lsevilla@ed.ac.uk |
Course secretary | Miss Lori Anderson
Tel: (0131 6)51 4164
Email: lori.anderson@ed.ac.uk |
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