Machine Vision (FSI-VSV-A)

Academic year 2025/2026
Supervisor: doc. Ing. Pavel Škrabánek, Ph.D.  
Supervising institute: ÚAI all courses guaranted by this institute
Teaching language: English
Aims of the course unit:

To acquaint students with basic principles of interaction of radiation with matter, with instrumentation for applications of computer vision in industry, and with image processing methods used in machine vision applications.

At the end of the course, the students will be able to:

  • select appropriate instrumentation for various machine vision applications,
  • design appropriate installation of the instrumentation,
  • create data processing parts of machine vision systems for basic machine vision applications.
Learning outcomes and competences:
 
Prerequisites:
 
Course contents:
 
Teaching methods and criteria:
 
Assesment methods and criteria linked to learning outcomes:

Knowledge and skills are verified by credit and examination. Credit requirements: elaboration of a given practical task. Attendance at lectures is recommended, while attendance at practical sessions is mandatory. Practical sessions that a student is unable to attend in the regular term can be made up during a substitute term. The exam is oral and covers the entire course material.
Controlled participation in lessons:
 
Type of course unit:
    Lecture  13 × 2 hrs. optionally                  
    Laboratory exercise  13 × 2 hrs. compulsory                  
Course curriculum:
    Lecture

  1. Introduction, interaction of radiation with matter, formation of images, parts of computer vision systems for industrial applications, typical applications of machine vision.

  2. Lighting geometry and its effect on the final image, radiation sources for machine vision, lighting in the visible, infrared and ultraviolet spectrums.

  3. Lenses with perspective projection - focal length, aperture and basic concepts related to perspective projection, intermediate rings, depth of field, lens defects and their compensation, lens resolution, telecentric lenses.

  4. Photodiode, CMOS sensors, electronic shutters, quantum efficiency of image sensors, formation of digital images, camera electronic circuits and their impact on noise in images.

  5. Optical filters and their use in machine vision, multispectral imaging, instrumentation for machine vision (line and area-scan digital cameras, light sources, optical filters, lenses).

  6. Design of the instrumentation part of a machine vision system (task processing, data collection and evaluation, documentation).

  7. Image histogram, intensity scale transformation, geometric transformations, interpolation.

  8. Introduction to spatial domain filtering, restoration of noise-affected images, edge detection.

  9. Image segmentation.

  10. Morphological transformations and their applications in image processing.

  11. Evaluation of processed images (shape detection, blob detection, measurement of distances and angles).

  12. Image classification.

  13. Object detection in images
    Laboratory exercise

  • Introduction to the subject matter, laboratory safety procedures

  • Installation and operation of illuminators, lens Installation and adjustment, working with optical filters

  • Connection and configuration of industrial cameras

  • Software for design and implementation of image processing pipelines

  • Design and implementation of a computer vision system for a given task
Literature - fundamental:
1. SZELISKI, Richard. Computer Vision: Algorithms and Applications [online]. 1. London: Springer, 2010 [cit. 2019-02-19]. Texts in computer science. ISBN 978-1-84882-935-0. Dostupné z: https://www.springer.com/gp/book/9781848829343
2. BATCHELOR, Bruce G. Machine vision handbook: with 1295 figures and 117 tables [online]. 1. London: Springer, [2012] [cit. 2019-02-19]. ISBN 978-1-84996-169-1. Dostupné z: https://link.springer.com/referencework/10.1007%2F978-1-84996-169-1
3. MCMANAMOM, Paul. Field Guide to Lidar. 1. Bellingham, Washington 98227-0010 USA: SPIE, 2015. ISBN 9781628416541.
4. A Practical Guide to Machine Vision Lighting. Automated Test and Automated Measurement Systems - National Instruments [online]. National Instruments, 2019, 30. ledna 2017 [cit. 2019-02-19]. Dostupné z: http://www.ni.com/white-paper/6901/en/
Literature - recommended:
1. HAVEL, Otto. Strojové vidění I: Principy a charakteristiky. Automa. Automa – časopis pro automatizační techniku, s. r. o., 2008, 14(1), 42-45. ISSN 1210-9592.
2. HAVEL, Otto. Strojové vidění II: Úlohy, nástroje a algoritmy. Automa. Automa – časopis pro automatizační techniku, s. r. o., 2008, 14(2), 54-56. ISSN 1210-9592.
3. HAVEL, Otto. Strojové vidění III: Kamery a jejich části. Automa. Automa – časopis pro automatizační techniku, s. r. o., 2008, 14(3), 42-44. ISSN 1210-9592.
4. HAVEL, Otto. Strojové vidění IV: Osvětlovače. Automa. Automa – časopis pro automatizační techniku, s. r. o., 2008, 14(4), 47-49. ISSN 1210-9592.
The study programmes with the given course:
Programme Study form Branch Spec. Final classification   Course-unit credits     Obligation     Level     Year     Semester  
N-AIŘ-P full-time study --- no specialisation -- Cr,Ex 5 Compulsory 2 1 S
N-AIŘ-P full-time study --- no specialisation -- Cr,Ex 5 Compulsory 2 2 S
N-ENG-Z visiting student --- no specialisation -- Cr,Ex 5 Recommended course 2 1 S