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The combination of the lectures, books, PowerPoints and assignments is a very good impetus to study·      The professor gave high quality lectures in spite of small amount of students·      Active arrangement of GPU from the professor·      Active arrangements and feedback for project demonstration from the professor·      Recommendations of conferences and workshops  Based on the feedback from the reference group, the general quality of the course is high.

The learning outcome of the course is achieved by the students attended the course. The learning outcomes have been explained to the students in the very first lectures of the course. I intentionally created a link between this course and the other courses relevant and which run in parallel to the course in terms of the projects the students worked on for each course. I made sure that the projects are complementary with the projects in the other courses. Also, when students get involved in project topics that are relevant to their MSc thesis topics, then they are more motivated to work on the project and solve its challenges.

The learning activities are in the form of ordinary course lectures, solving small exercises during the course or at home, solving bigger exercises to be graded, working on a bigger course project, making several presentations during the semester related to the project, making implementation of the project and demonstration of it for grading, writing and delivering a report for the project. The students were able to keep up with the pace, and these work contributed to the students understanding of the concepts of the course and since there was high interaction with the students and me, the students were motivated to bring in the requirements of the course in a timely manner. The learning was carried out fully online and it worked well. Regarding carrying out more matlab exercises, in the coming years, there will be made possible.

Based on gradings of assignment and a project delivery. In the project the students use the concepts, algorithm and tools learned during the course and for that reason both the assignments and the project help students to achieve the learning outcomes. The lectures support the theoretical and practical aspects.

NTNU:TIA:REST

Institutt for datateknologi og informatikk

Department of Computer Science

Ingen hjelpemidler tillatt.

Engelsk

Anbefalte forkunnskaper

Mer om vurdering

Faglig innhold

Kursmateriell

Læringsformer og aktiviteter

Læringsutbytte

Image Processing and Analysis

Informatikk

Master's Programme in Computational Colour and Spectral Imaging

Master in Applied Computer Science

Master in Applied Computer Science-deltid

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Image Processing and Analysis - IMT4305