Applied Image Analysis
PhD School at the Faculty of SCIENCE at University of Copenhagen
This is a toolbox course where 80% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% og the seats are reserved to PhD students from other Danish Universities/faculties (except CBS). Seats will be allocated on a first-come, first-served basis and according to the applicable rules.
Anyone can apply for the course, but if you are not a PhD student at a Danish university (except CBS), you will be placed on the waiting list until enrollment deadline. After the enrollment deadline, available seats will be allocated to applicants on the waiting list.
Aim and Content
The Applied Image Analysis course is targeted at PhD researchers outside computer science who, in their research, face the problem of image analysis.
The students are expected to bring their image analysis problems to the course, where these problems will be analyzed during lectures and group work. The course will familiarize the students with basic image analysis concepts, including image preprocessing, annotation and analysis.
The student will get familiarized with existing image analysis tools and will also be asked to develop some simple programming snippets.
We will rely on Python public libraries for image processing and analysis.
The students need to bring their laptops to the course.
Finally, we will focus on practical considerations when working with image analysis.
Learning outcomes
Intended learning outcome for the students who complete the course:
Knowledge
• Basic knowledge of image processing framework design.
• Basic knowledge of a scripting language such as Python relevant to image processing and how they can be applied to automate tasks in digital image analysis.
• Basic knowledge of a scripting language such as Python relevant to image processing and how they can be applied to automate tasks in digital image analysis.
• Basic knowledge of the integration of artificial intelligence techniques for image analysis.
• Understanding of how statistical methods are applied in the classification of objects in images.
Skills
• Write short programs that perform essential image processing tasks such as noise reduction, segmentation, and object analysis on 2-dimensional images.
• Applying AI for image analysis.
• Analyzing geometrical features to describe and understand their characteristics.
• Skills in using statistical methods effectively for the classification of objects in images, enabling more accurate analysis and interpretation of image data.
Competences
• Critically choose and apply basic digital image analysis techniques to research data, and evaluate the effectiveness of these techniques.
Target Group
PhD students from disciplines outside computer science interested in learning about, applying and using image analysis methods by scripting in their research.
Recommended Academic Qualifications
Some programming experience is preferred, but not a requirement. We will use basic mathematics at the level of introduction to mathematics and statistics in a first-year science bachelor's education.
Research Area
Image processing for a broad group of scientific fields.
Teaching and Learning Methods
The course consists of four components: Introduction to image analysis problems, introduction to scripting, processing digital images, and analysis of images using artificial intelligence.
Teaching will be based on problem-based learning and composed of supervised sessions in combination with lectures.
Type of Assessment
Written feedback on final report.
Literature
Literature will be distributed online.
Course coordinator
Bulat Ibragimov, Associate Professor, bulat@di.ku.dk
Guest Lecturers
Guests will be invited when relevant.
Dates
Course dates (full course days):
2nd of February 2026
9th of March 2026
13th of April 2026
11th of May 2026
Course location
Nørre Campus. Potentially online.
Requirements for signing up
A month prior to the course start. I am planning to contact the students and prepare the descriptions of the image analysis problems they have, so we can discuss them during the course. It can be in the form of a motivation letter.
Course fee
• PhD student enrolled at SCIENCE: 0 DKK
• PhD student from Danish PhD school Open market: 0 DKK
• PhD student from Danish PhD school not Open market: 3000 DKK
• PhD student from foreign university: 3000 DKK
• Master's student from Danish university: 0 DKK
• Master's student from foreign university: 3000 DKK
• Non-PhD student employed at a university (e.g., postdocs): 3000 DKK
• Non-PhD student not employed at a university (e.g., from a private company): 8400 DKK
Cancellation policy
• Cancellations made up to two weeks before the course starts are free of charge.
• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000
• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000
• No-show will result in a fee of DKK 5.000
• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000
Course fee and participant fee
PhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.
In addition to the course fee, there might also be a participant fee.
If the course has a participant fee, this will apply to all participants regardless of participant
type - and in addition to the course fee.