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13M041MV - Machine Vision

Course specification
Course title Machine Vision
Acronym 13M041MV
Study programme Electrical Engineering and Computing
Module Electronics
Type of study master academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
    Lecturer/Associate (for OTC)
    ESPB 6.0 Status elective
    Condition Passed exam in subject Digital Image Processing on undergraduate academic studies.
    The goal The aim of the course is to introduce to the students the methods and technologies used in automated systems for measurement and control based on image processing. Special attention is paid to development and implementation of image processing algorithms by means of modern development environments and advanced hardware platforms with accent on industrial applications.
    The outcome Students will gain basic knowledge about methods and elements used in machine vision systems, and procedures of integration of hardware and software components targeting specific functional demands. The acquired knowledge will enable students to independently analyze, project and implement industrial machine vision systems.
    Contents
    Contents of lectures Functions, architecture and basic machine vision components. Image formation, image sensors, hyperspectral imaging. Optics. Illumination. Interfacing sensors and subsystems for image processing. Compact systems. Stereo vision. Software and hardware implementations of image processing algorithms. Integration of machine vision elements in laboratory model of industrial machine vision system.
    Contents of exercises Development of machine vision application based on laboratory model comprised of image processing hardware, model of industrial process and image acquisition equipment.
    Literature
    1. Davies, E.R., 2012. Computer and machine vision: theory, algorithms, practicalities. Academic Press. (Original title)
    2. Hornberg, A. ed., 2007. Handbook of machine vision. John Wiley & Sons. (Original title)
    3. Snyder, W.E. and Qi, H., 2010. Machine vision. Cambridge University Press. (Original title)
    4. Dawson-Howe, K., 2014. A practical introduction to computer vision with OpenCV. John Wiley & Sons. (Original title)
    Number of hours per week during the semester/trimester/year
    Lectures Exercises OTC Study and Research Other classes
    3 2
    Methods of teaching Lectures. Excersises and student project on laboratory model.
    Knowledge score (maximum points 100)
    Pre obligations Points Final exam Points
    Activites during lectures Test paper
    Practical lessons Oral examination 40
    Projects
    Colloquia 20
    Seminars 40