Undergraduate Course: Computer Vision (INFR11212)
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 | 20 |
ECTS Credits | 10 |
| Summary | **This course replaces Image and Vision Computing (INFR11140) and Advanced Vision (INFR11031)**
Computer Vision is the field of study that teaches computers how to 'see'. This means, how to go from the pixels in an image to the information that a human can describe when they see a picture, much like self-driving cars, autonomous robots, or social media apps that recommend images or videos based on your preferences. This course is an in-depth introduction to the field of Computer Vision.
The course is structured around different problems in computer vision, such as object recognition and video classification, and covers both classical and deep learning approaches.
The course can be taken without any prior knowledge of computer vision or deep learning, but it does assume some familiarity with machine learning concepts, and relevant mathematics and programming skills (see details under "Other Requirements"). The course delivers both theoretical and practical knowledge, and by the end you should be able to understand, design, and implement computer vision techniques for many real-world problems. |
| Course description |
This course teaches the principles and methodologies of computer vision, both from a theoretical and a practical perspective.
The content includes first the basics of image formation, image processing, classic computer vision principles and methods and introduction to deep learning. In the second part with focus on applications, including generative models, 3D, video understanding, vision and language, etc.
The course will be taught as a combination of:
- Live lectures.
- Tutorials to develop your ability to solve vision problems from a theoretical perspective.
- Lab sessions to develop practical skills. The coursework will be structured as a series of small non-assessed practice sessions, which will build up the skills for the assessed mini-project at the end of the course.
The tentative syllabus is:
- Foundations of Computer Vision
- Image Formation
- Image Processing
- Classic Vision
- Deep Learning
- Generative Models
- Geometry and 3D
- Video Understanding
- Vision and Language
- Ethics
- Learning with Limited Data
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | Students MUST NOT also be taking
Image and Vision Computing (INFR11140) OR
Advanced Vision (Level 11) (INFR11031)
|
Other requirements | Students should check these maths and programming requirements carefully, as the course assumes and builds on these foundations. Experience has shown that students without this background can struggle with the course.
Maths requirements:
1. Linear algebra: Vectors: scalar (dot) product, transpose, unit vectors, vector length, orthogonality. Matrices: addition, matrix multiplication, matrix inversion. Eigenvectors, determinants quadratic forms
2. Special functions: properties and combination rules for logarithm and exponential
3. Calculus: Rules for differentiation of standard functions. Functions of several variables. Partial differentiation. Multivariate maxima and minima
4. Geometry: Basics of lines, planes and hyperplanes. Coordinate geometry of circle, sphere, ellipse, ellipsoid and n-dimensional generalizations
5. Probability theory: Discrete and continuous univariate random variables. Expectation, variance. Univariate Gaussian distribution. Joint and conditional distributions
Machine Learning requirements:
This course assumes students are familiar with concepts from machine learning such as supervised training, feature selection, loss functions, and optimization. It is strongly recommended that students who register for this course have either taken a machine learning course previously or are registered for one in Semester 1.
Programming requirements:
Students should be familiar with programming in a modern object-oriented language, ideally Python which is the course language. |
Information for Visiting Students
| Pre-requisites | As above. |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2024/25, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
176 )
|
| Assessment (Further Info) |
Written Exam
50 %,
Coursework
50 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
50% exam
50% coursework |
| Feedback |
Written feedback on coursework |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | Computer Vision (INFR11212) | 2:120 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Define and explain principles underpinning computer vision methods
- Describe current vision problem settings and their current solutions
- Implement, train and debug computer vision models
- Design, explain, analyse, and compare the behaviour of computer vision models under different settings
- Identify social and ethical implications of computer vision methods in the real world
|
Reading List
Foundations of Computer Vision (Torralba, Isola, Freeman). This will be the main textbook. It is more comprehensive than the scope of the course.
Computer Vision (Rick Szeliski)
Deep Learning (Yoshua Bengio) |
Additional Information
| Graduate Attributes and Skills |
Research and enquiry:
- Problem-solving, debugging and understanding the behavior of systems under different scenarios
- Critical / analytical thinking of methods, their advantages and disadvantages
- Describing partial evidence or results, and reasoning under uncertainty
Personal effectiveness:
- Planning and organizing own time, to achieve milestones at particular deadlines
Personal responsibility and autonomy:
- Learning independently, researching how others have addressed the same issue
- Develop creativity to address problems in existing methods
Communication:
- Work is done in pairs, so interpersonal/teamwork skills will be developed
- The coursework involves writing a report, so verbal and written communication will be developed |
| Keywords | Computer Vision,Deep Learning,Image Processing |
Contacts
| Course organiser | Ms Laura Sevilla-Lara
Tel:
Email: lsevilla@ed.ac.uk |
Course secretary | Miss Yesica Marco Azorin
Tel: (0131 6)50 5194
Email: ymarcoa@ed.ac.uk |
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