19M061IVS - Industrial vision systems

Course specification
Course title Industrial vision systems
Acronym 19M061IVS
Study programme Electrical Engineering and Computing
Type of study master academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
    ESPB 6.0 Status elective
    Condition none
    The goal The aim of this course is to introduce students to the design, realization and application of the industrial vision systems.
    The outcome Following the successful completion of this course, students will be able to define technical requirements and to integrate industrial vision system with the production process. Also students will be familiar with other applications of image-based control systems.
    Contents of lectures Introduction to designing a industrial vision system (IVS). Planning the lighting system. Optical filters and lenses. Cameras - interfaces, working parameters, triggers, noise. 3D Image Acquisition Devices. 3D Reconstruction. Template Matching. Calibration of Camera and Robot. Using Convolutional Neural Networks in IVS. IVS for Self-Driving Cars. Applications of industrial vision system.
    Contents of exercises Solving selected problems using Python or C++ programming environment and the OpenCV library. Introduction to standard software packages for industrial vision applications (LabVIEW, Adaptive Vision Studio). Application of standards steps for IVS: positioning, verification, measurements, mark identificaton, defect detectoin, color comparison.
    1. C. Steger, M. Ulrich, C. Wiedemann, "Machine Vision Algorithms and Applications", Wiley-VCH, 2018. (Original title)
    2. C. Demant, B. Streicher-Abel, C. Garnica, "Industrial Image Processing - Visual Quality Control in Manufacturing", Springer, 2013. (Original title)
    3. Alexander Hornberg, "Handbook of Machine and Computer Vision - The Guide for Developers and Users", Wiley-VCH, 2017. (Original title)
    4. A. Kaehler, G. Bradski, "Learning OpenCV 3", O’Reilly Media, 2017. (Original title)
    5. B. G. Batchelor, "Machine Vision Handbook", Springer, 2012. (Original title)
    Number of hours per week during the semester/trimester/year
    Lectures Exercises OTC Study and Research Other classes
    3 1
    Methods of teaching lectures, exercises, PC exercises, independent work
    Knowledge score (maximum points 100)
    Pre obligations Points Final exam Points
    Activites during lectures Test paper
    Practical lessons 30 Oral examination 40
    Seminars 30