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.

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.