Recommended prerequisite for participation in the module

This module is based on knowledge gained on the 1st Semester of the MSc in the Manufacturing Technology programme.

Content, progress and pedagogy of the module

Learning objectives

Knowledge

• Have gained an understanding of relevant technologies enabling the design of intelligent machine vision modules
• Have gained an understanding of the basic steps of image processing techniques and how they can be integrated into machine vision solutions
• Have gained an understanding of how to develop and simulate advanced machine vision solutions
• Have gained an understanding of how to select the right optical sensors and fuse multiple modalities (lidar, 2D & 3D vision, 3D point clouds) for a manufacturing module.
• Have knowledge about the business potential of machine vision solutions.

Skills

• Be able to integrate various technologies to provide manufacturing systems with intelligent capabilities (e.g. image processing and understanding, feature extraction, image segmentation and classification, product localization, visual inspection, anomaly detection)
• Be able to integrate and implement machine vision into a small and limited manufacturing system.
• Be able to collect data and train an intelligent machine vision module to adapt to changes in production.

Competences

• Be able to professionally participate in projects aiming at developing advanced machine vision modules.
• Establish the foundation for applying machine vision and relevant simulation tools for future research and development activities.

Type of instruction

The teaching is organized in accordance with the general form of teaching. Please see the programme curriculum §17.

Extent and expected workload

Since it is a 5 ECTS course module the expected workload is 150 hours for the student.

Exam

Exams

Name of exam	Applied Machine Vision
Type of exam	Written or oral exam
ECTS	5
Permitted aids	Information about allowed helping aids for the examination will be published in the description of the semester/module.
Assessment	7-point grading scale
Type of grading	Internal examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures