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
Pass with distinction, Pass with credit, Pass, Fail
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 .

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