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.
Learning outcomes
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 and 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®.
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