Application deadline

Applicants from countries within EU/EEA: June 1st for the autumn semester and December 1st for the spring semester.

Exchange students and Fulbright students: 1 October for the spring semester and 15 April for the autumn semester.

Type of course

Admission requirements

A relevant undergraduate Bachelor Engineering program with minimum 25 credits mathematics, 5 credits statistics, 7,5 credits physics

Application code: 9371

Course content

Introduction to machine vision: fundamentals of image formation and cameras. Fundamentals of vision sensors (visible, infrared, multi-spectral). Image processing: image filtering, edge detection, image segmentation. Image analysis using pattern detection and recognition methods.

Visualization of image analysis methods. Image processing & analysis using MATLAB®. Practical labs on machine vision. Case study in one of the following areas: automation, drone technology, medical informatics & imaging, nautical science, process & gas technology, remote sensing, and industrial applications.

Objectives of the course

Knowledge:

This interdisciplinary course should give the candidate a deep understanding of machine vision with special focus on a case study in one of the following areas: Automation, Drone Technology, Medical Informatics & Imaging, Nautical Science, Process & Gas Technology, Remote Sensing, and Industrial Applications.

Skills:

• Candidate will build knowledge in image formation, cameras, and vision sensors.
• Candidate will learn about image processing operations such as filtering, edge detection and image segmentation.
• Candidate will also learn state-of-the-art methods for image analysis (i.e., pattern detection and recognition).
• Candidate should be able to use state-of-the-art image analysis methods in practical problems.
• Candidate will learn the use of correct visualization tools for the image analysis problems.
• The candidate should be able to understand and use the knowledge from machine vision in their selected domain.
• The candidate should be able to demonstrate their knowledge using MATLAB® or Python.

Language of instruction and examination

Teaching methods

Information to incoming exchange students

This course is open for inbound exchange student who meets the admission requirements. Please see the Admission requirements" section

Do you have questions about this module? Please check the following website to contact the course coordinator for exchange students at the faculty: https://en.uit.no/education/art?p_document_id=510412

Assessment

Coursework: Participation in workshops and laboratory work.

Examination:

One combined grade will be given based on case study report and oral exam.

Grading scale: Letter grading A - F, F is fail.

Continuation Examination: Students who have not passed - or have not submitted their reports in time due to legitimate reasons will be given an extended submission deadline in the following semester.