Course content

The course contains the following topics:

• Fundamental image analysis
• Fundamental 3D modelling
• Practical use of standard libraries for machine vision
• Object recognition and tracking
• 3D reconstruction from stereo views
• Other topics required for achieving intended learning outcomes

Learning outcome

Knowledge

• The candidate can explain fundamental mathematical models for digital imaging, 3D models, and machine vision.
• The candidate are aware of the principles of digital cameras and image capture.

Skills

• The candidate can implement selected techniques for object recognition, tracking and 3D reconstruction.

General competence

• The candidate has a good analytic understanding of machine vision, and the relationship between different approach to machine vision, and the collaboration between machine vision and other systems in robotics.
• The candidate can exploit the connection between theory and application for presenting and discussing engineering problems and solutions.

Learning methods and activities

Learning activities include seminars, lectures, tutorials and lab/project work.

A constructivist and hermeneutic approach for learning is endorsed, with focus on problem solving and practical application of theory.

Further on evaluation

Assessment guidelines for the oral exam will be discussed with the reference group and published before the end of the teaching term. The re-sit exam is an oral exam the following spring.

Specific conditions

Recommended previous knowledge

• ISTA1002 Statistikk
• IMA2011 Matematiske metoder 2
• IMA1001 Matematiske metoder 1

Required previous knowledge

The course has no prerequisites.

Course materials

An updated course overview, including curriculum, is presented at the start of the semester.