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 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.
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.
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
|Academic year 2021/22, Available to all students (SV1)
|Course Start Date
|Learning and Teaching activities (Further Info)
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
|Assessment (Further Info)
|Additional Information (Assessment)
||There is one coursework assignment worth 50%. The exam is worth 50%.
||Hours & Minutes
|Main Exam Diet S2 (April/May)||2:00|
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
|Aaron Courville, Ian Goodfellow, and Yoshua Bengio, Deep Learning|
|Course organiser||Ms Laura Sevilla-Lara
|Course secretary||Miss Clara Fraser