Admission requirements
Assumed Prior Knowledge
The below list indicates assumed prior knowledge for this course, and undergraduate courses where you may have obtained it:
Working knowledge of Python (Introduction to Programming)
Linear algebra (Linear Algebra for Computer Scientists 1 & 2)
Calculus (Calculus 1 & 2)
Basic knowledge of machine learning (Machine Learning)
Description
The aim of this unit is to give you an introduction to computer vision: the theory, principles, techniques, algorithms and applications. The course covers topics from early to mid-level vision, i.e. the analysis and enhancement of images/videos, and high-level vision facilitating the understanding of the content of images/videos. Key algorithms will be covered ranging from classical (e.g. Gaussian and Laplacian Pyramids) to contemporary (e.g. (CNNs, GANs). Application areas of computer vision are far-reaching and wide, from data compression to measuring the quality of performing actions by humans. The techniques in image processing and computer vision may be used in autonomous driving, medical imaging, CGI, remote sensing, pedestrian behaviour analysis, facial recognition and regeneration, traffic analysis, biometrics, product quality assurance, and much more.
Course objectives
Upon successful completion of the course students will be able to:
Understand low-level image processing methods such as filtering and edge detection
Understand the principles of different deep neural networks techniques and their applications to visual data
Understand core computer vision tasks for instance, matching, segmentation, detection, tracking and generation
Understand the ethical and privacy-related implications of large datasets and models.
Apply mathematical techniques learnt in prior courses (including linear algebra and calculus) to solve problems in computer vision.
Implement common existing models for computer vision tasks
Analyze the advantages and disadvantages of different computer vision algorithms
Relate and compare the possible techniques to solve different computer vision problems
Timetable
The most updated version of the timetables can be found on the students' website:
In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.
Additionally, you can easily link MyTimetable to a calendar app on your phone, and schedule changes will be automatically updated in your calendar. You can also choose to receive email notifications about schedule changes. You can enable notifications in Settings after logging in.
Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.
Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.
Mode of instruction
Weekly lectures
Practical session
Assignments
Assessment method
Written exam (60%)
Practical assignments (40%)
- Two assignments of 10%
- One assignment of 20%
The grade for the written exam should be 5.5 or higher in order to complete the course. The average grade for the practical assignments should be 5.5 or higher in order to complete the course. If one of the tasks is not submitted the grade for that task is 0. Each assignment has a re-sit opportunity (a later submission). The maximum grade for a re-sit assignment is 6.
The teacher will inform the students how the inspection of and follow-up discussion of the exams will take place.
Reading list
Slides contain all the necessary material for the course. For a few lectures, additional material will be made available through the course webpage.
The following books are recommended but not mandatory for the course:
Foundations of Computer Vision, Antonio Torralba, Phillip Isola and William T. Freeman.
Computer Vision: Algorithms and Applications, 2nd edition draft, Richard Szaliski (available for free online)
Registration
As a student, you are responsible for enrolling on time through MyStudyMap.
In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.
There are two enrolment periods per year:
Enrolment for the fall opens in July
Enrolment for the spring opens in December
See this page for more information about deadlines and enrolling for courses and exams.
Note:
It is mandatory to enrol for all activities of a course that you are going to follow.
Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.
Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.
Contact
Remarks
Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.