ITF31719 Image Processing (Spring 2027)
Facts about the course
- ECTS Credits:
- 10
- Responsible faculty:
- Faculty of Computer Sciences, Engineering and Economics
- Campus:
- Halden
- Course Leader:
- Sukalpa Chanda
- Teaching language:
- Norwegian or English
- Duration:
- ½ year
Table of contents
The course is affiliated to the following study programme
Compulsory course in Bachelor in Computer Science - Specialisation in Machine Learning (year 2024 and 2025)
Elective course in
- Bachelor in Computer Science (year 2026)
- Bachelor in Computer Engineering
- Bachelor in Computer Engineering, TRESS
Recommended previous knowledge
This course requires prerequisite knowledge in:
- mathematics equivalent to the course in Mathematics for Computer Science / Discrete Mathematics
- mathematics equivalent to the course in Mathematics 1 / Calculus
- programming equivalent to the course in Object-oriented Programming / Programming 2
Teaching semester
Content
The student's learning outcomes on passing the course
Knowledge
The student
- understands digital images and their properties
- is familiar with representation methods for digital images, including image coding and compression
- understands how images can be filtered/processed for better quality
- understands the principles and methods in basic pattern recognition
Skills
The student can
- use standard filters to filter noise
- use standard filters to enhance the details in an image
- use standard techniques to detect edges, corners and objects in an image
- write programs for basic image processing and pattern recognition
- get a computer and/or computer system to recognize objects in an image
Teaching and learning methods
Lectures, exercises and lab assignments/projects.
If students from international partners attend courses, the lectures will be conducted in English.
Workload
Approx. 250 hours.
4 hours of lectures per week, in addition to study groups and project work.
Exam
Individual portfolio assignment
2 assignments and 1 portfolio project must be submitted by given deadlines and in accordance with specifications given by the course instructor.
An individual grade will be awarded on the basis of assessment of the two assignments (in total 50%) and the portfolio project (50%) as a whole.
Grading scale from A to F are used.
Examiners
Conditions for resit/rescheduled exams
In the event of a resit or rescheduled examination, all parts of the portfolio must be re-submitted.
Resit / rescheduled exam is carried out at the same time as the next ordinary examination. The department management can nevertheless decide to arrange a resit exam in the following semester.
Course evaluation
This course is evaluated by a
- End-of-term evaluation
The responsible for the course compiles a report based on the feedback from the students and his/her own experience with the course. The report is discussed by the study quality committee of the faculty of Computer Sciences.