Computer-Assisted Image Analysis II
Syllabus, Master's level, 1TD398
- Code
- 1TD398
- Education cycle
- Second cycle
- Main field(s) of study and in-depth level
- Computer Science A1F, Technology A1F
- Grading system
- Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Finalised by
- The Faculty Board of Science and Technology, 30 August 2018
- Responsible department
- Department of Information Technology
Entry requirements
120 credits including Computer-Assisted Image Analysis I or equivalent. At least 5 credits in mathematical statistics. Proficiency in English equivalent to the Swedish upper secondary course English 6.
Learning outcomes
On completion of the course, the student should be able to:
- describe and use advanced filtering methods for noise reduction and edge enhancement;
- describe and use several sophisticated segmentation methods, image based as well as model based;
- describe digital topology and geometry in 2 and 3 dimensions;
- apply classification algorithms to interpret image contents;
- apply different techniques for quality assessment of segmentation, quantitative analysis, and classification.
Content
Methods for solving problems in image analysis. Analysis of 3D images (volume, stereo, and time series). Image enhancement. Image segmentation and border detection. Registration of images, search methods and optimisation. Digital geometry and mathematical morphology. Computer vision. Classification. . Deep learning for image segmentation and classification.
Instruction
Lectures, laboratory work, tutoring.
Assessment
Written examination, oral and written presentation of assignments.
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.