Computer Engineering MA, Image Analysis, 6 credits

Aim

The course aims to give a good understanding of theoretical concepts and practical methods in image analysis, using traditional methods, machine learning and deep learning.

Course objectives

After a finished course the student shall be able to:
- describe fundamental concepts, terminology, models and methods in image analysis, using traditional and learning-based methods (machine and deep learning).
- apply acquired knowledge within the fields of image analysis to solve relevant problems with images from different modalities.
- implement a number of methods/techiques within image analysis using learning-based approaches.
- systematically evaluate algorithms within the fields of image analysis with respect to pros and cons for different tasks and problems.

Content

- Preprocessing Techniques: Filtering, edge detection, feature detection, and image transformations
- Histogram operations for image enhancement and restoration
- Analysis of images in the frequency domain
- Contours and skeleton analysis
- Machine learning in image analysis: Learning, testing, automated analysis of large datasets, and limitations of machine learning in image analysis
- Machine learning and neural networks-based image segmentation, feature extraction, shape analysis, and motion analysis
- Ethics in experimental design and evaluation of images
- Software tools and practical applications: Introduction to image analysis software and hands-on exercises
- Evaluation of image analysis systems
- Applications and case studies: Examples from research and industry

Entry requirements

Computer or Electrical Engineering, 60 credits, including programming, 10 credits, and Signal and Image Processing, 6 credits, and Computer Vision, 6 credits; Mathematical subjects, 30 credits, including probability theory and statistics, and linear algebra.

Teaching form

Lectures, and a project

Examination form

P101: Project, with written report, 3 Credits
Grade scale: Seven-grade scale, A-F o Fx

T101: Written exam, 3 Credits
Grade scale: Seven-grade scale, A-F o Fx

Link to grading criteria: https://www.miun.se/gradingcriteria.

The examiner has the right to offer alternative examination arrangements to students who have been granted the right to special support by Mid Sweden University’s disabilities adviser.

Examination restrictions

Students are entitled to three examination opportunities within one year according to the examination format given in this version of the course syllabus. After the one-year period, the examination format given in the most recent version of the course syllabus applies.

Grading system

Seven-grade scale, A-F o Fx