Computer-Assisted Image Analysis II
10 credits
Syllabus, Master's level, 1TD398
A revised version of the syllabus is available.
- 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, 3 May 2017
- 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.
Learning outcomes
To pass, 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 .