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Sensor Technology and Image Analysis, 7.5 Higher education credits

Sensorteknik och bildanalys, 7,5 Högskolepoäng

Established: 2023-06-07

Established by: Department of Engineering Science

Applies from: H23

Learning outcomes

Knowledge and understanding

After completing the course the student shall show the ability to explain:

• the functionality, properties, and limitations of common sensors used in automation, monitoring, and process control.
• the basic concepts in digital image analysis.
• the basic concepts in image pattern classification.

Skills and abilities

After completing the course the student shall show the ability to:

• implement and evaluate an image analysis algorithm.
• implement and evaluate an image pattern classification algorithm.
• propose a suitable sensor system for a specific situation.
• identify weaknesses and limitations in different sensor systems applied in automation, monitoring, 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 in the Swedish Upper Secondary School (high school) or equivalent.

The forms of assessment of student performance

Individual laboratory assignment with oral presentation. Group project with oral presentation. Individual written examination.

Course contents

The course consists of three main parts, sensors, image analysis, and image pattern recognition.

Within sensors, various concepts within optical sensors are treated, as well as the functionality and properties of a number of common sensors within automation, monitoring, and control. Examples of sensors that are treated are cameras, laser distance sensors, spectrometers, and temperature sensors for non-contact measurement.

In image analysis, the basic properties of digital images are first addressed. After this, the focus is on finding features in images using, for example, image segmentation. In addition to this, camera calibration is also covered.

In image pattern recognition, methods based on prototype matching, optimal statistical formulation, and neural networks are treated.

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

A1N - second cycle, has only first-cycle course/s as entry requirements

Main field of study

Automation, Production Technology