Course detail

Machine Vision

FSI-VSV-AAcad. year: 2026/2027

The course is aimed at a digital photography fundamentals and processing of digital images within computer vision systems. The course focus at the specifics of the computer vision in terms of lighting and capturing of scenes.

Language of instruction

English

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. Ing. Pavel Škrabánek, Ph.D.

Department

Institute of Automation and Computer Science (ÚAI)

Entry knowledge

Expected to have basic knowledge of algorithms, programming, and of fundamental concepts in mathematics and physics.

Rules for evaluation and completion of the course

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.

Aims

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.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

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/
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
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

Recommended reading

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.
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.
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.
HAVEL, Otto. Strojové vidění IV: Osvětlovače. Automa. Automa – časopis pro automatizační techniku, s. r. o., 2008, 14(4), 47-49. ISSN 1210-9592.

Classification of course in study plans

  • Programme N-AIŘ-P Master's 1 year of study, summer semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. Pavel Škrabánek, Ph.D.

Syllabus

  1. Introduction to machine vision: Interaction of radiation with matter, image formation, components of machine vision systems, typical applications.
  2. Lighting in machine vision: Lighting geometry and its influence on the resulting image, radiation sources, lighting in the visible, infrared, and ultraviolet spectra, lighting for industrial applications.
  3. Lenses and optical systems: Perspective projection, focal length, aperture, extension tubes, depth of field, lens aberrations and their compensation, lens resolution, telecentric lenses, optical filters.
  4. Image sensors: Photodiodes, CMOS sensors, types of electronic shutters, quantum efficiency, digital image formation, spatial resolution, multispectral imaging.
  5. Industrial cameras: Line-scan and area-scan cameras, specifications of industrial cameras and lenses, pixel binning, standard communication interfaces, synchronization of cameras and lighting, electronic circuits and their impact on noise.
  6. Design of the hardware part of a machine vision system: Requirement analysis, data collection and evaluation, component selection, 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. Review

Laboratory exercise

26 hod., compulsory

Teacher / Lecturer

doc. Ing. Pavel Škrabánek, Ph.D.

Syllabus

  • 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