ITD33517 Image Processing and Pattern Recognition (Spring 2019)
Facts about the course
- ECTS Credits:
- 10
- Responsible faculty:
- Avdeling for informasjonsteknologi
- Course Leader:
- Lars Vidar Magnusson
- Teaching language:
- See Forms of teaching and learning
- Duration:
- ½ year
Table of contents
The course is affiliated to the following study programme
Recommended previous knowledge
Teaching semester
Content
The student's learning outcomes on passing the course
Knowledge:
On completion of the course, 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 affected by noise
- understands how images can be filtered/processed for better quality
- understands the principles and methods in basic pattern recognition
Skills:
On completion of the course, the student can:
- use standard filters to improve image quality by filtering 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. 240 hours.
4 hours of lectures per week, as well as study groups and lab assignments/projects.
Exam
Individual portfolio assignment
6 assignments must be submitted by given deadlines and in accordance with specifications issued by the course instructor. An individual grade will be awarded on the basis of an overall assessment.
Grading scale from A to F are used.
Examiners
Conditions for resit/rescheduled exams
Course evaluation
This course is evaluated by a
- Mid-term evaluation (compulsory)
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.
Reading list
The literature list was last updated November 16, 2017
Web-based resources will be available on the learning platform.
Recommended literature:
Gonzalez, R. C and Woods, R. E: Digital Image Processing. Pearson. 3. edition. ISBN 978-0131687288.