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Machine Vision and AI, 6 Credits

Machine vision och AI, 6 Högskolepoäng

Established: 2025-10-30

Established by: Department of Engineering Science

Applies from: V26

Learning outcomes

After completing the course, the student should be able to:

Knowledge and understanding

• explain fundamental concepts in image pattern recognition using artificial intelligence (AI).
• describe various types of AI algorithms and their application in machine vision.
• understand the principles behind mathematical projection, geometric transformations, and calibration in a vision based robot system.
• demonstrate insight into how AI algorithms are affected by data quality and how this impacts system performance.

Competence and skills

• use existing libraries and tools to carry out AI tasks within machine vision.
• handle 3D machine vision techniques, including filtering, detail detection, and object matching.
• implement navigation and tracking in both 2D and 3D environments for robot-based systems.

Judgement and approach

• reflect on the strengths, weaknesses, and limitations of various vision systems used in automation, surveillance, and process control.

Entry requirements

Degree of Bachelor of Science in computer engineering, electrical engineering, mechanical engineering or industrial engineering and management. Additionally, the Bachelor of Science degree must be comprised of a minimum of 5 HE credits in programming and 15 HE credits in mathematics. Verified knowledge of English corresponding to the course English 6 or English level 2 in the Swedish Upper Secondary School (high school) or equivalent.

Additionally required an approved result from the following course/courses: RBK600 Robot Certificate and RCS600 Robotic Systems or the equivalent.

The forms of assessment of student performance

Laboratory work in groups with oral presentation. Project work in groups with individual oral presentation

Course contents

The course provides an introduction to image pattern recognition using Artificial Intelligence (AI). It offers foundational knowledge of key concepts, algorithms, and techniques used to analyze and interpret visual data in automated systems. The course covers various types of AI algorithms for navigation and tracking in both two-dimensional and three-dimensional environments for robot-based systems.

Other regulations

Course grading: F/Fx/E/D/C/B/A - Insufficient, Insufficient- more work required before the credit can be awarded, Sufficient, Satisfactory, Good, Very Good, Excellent

Course language: The teaching is conducted in English.

General rules pertaining to examination at University West are available at www.hv.se.

If the student has a decision/recommendation on special support due to disability, the examiner has the right to examine the student in a customized examination form.

Cycle

Second cycle

Progressive specialization

A1F - Second cycle, has second-cycle course/s as entry requirements

Main field of study

Automation, Production Technology