Course Objectives

Students will be introduced to theory, coding and practical use of image processing, image analysis, pattern-recognition and classification within the various areas of use.

Course Description

Pre-presentation of digital images, noise reduction, edge detection, segmentation, measurement and object characteristics, and classification based on such characteristics. The course will show how these techniques may be used, and how they can be combined in partly automated analysis sequences.

Learning Methods

6-7 hours of lectures, exercises and guidance per week in the autumn semester.

Assessment Methods

Mid-term assessment in the form of obligatory assignments which must be approved before students will be permitted to sit the individual, written 4-hour final examination.

Minor adjustments may occur during the academic year, subject to the decision of the Dean