Course detail

Machine Vision

FSI-VSV-KAcad. year: 2022/2023

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

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

prof. RNDr. Ing. Jiří Šťastný, CSc.

Department

Institute of Automation and Computer Science (ÚAI)

Learning outcomes of the course unit

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.

Prerequisites

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

Co-requisites

Not applicable.

Planned learning activities and teaching methods

The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focused on practical topics presented in lectures.

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 seminars is obligatory. Examination is oral and it covers the whole curriculum. 

Course curriculum

Not applicable.

Work placements

Not applicable.

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. 

Specification of controlled education, way of implementation and compensation for absences

Attendance at lectures is recommended, attendance at seminars is obligatory and checked. Absences can be compensated for by attending a seminar with another group in the same week, or at the end of semester within a special seminar.

Recommended optional programme components

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
MCMANAMOM, Paul. Field Guide to Lidar. 1. Bellingham, Washington 98227-0010 USA: SPIE, 2015. ISBN 9781628416541.
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.

Elearning

eLearning: currently opened course

Classification of course in study plans

  • Programme N-AIŘ-K Master's 2 year of study, winter semester, compulsory

Type of course unit

 

Guided consultation in combined form of studies

9 hod., compulsory

Teacher / Lecturer

doc. Ing. Pavel Škrabánek, Ph.D.

Syllabus

1.Basic principles of digital imaging
2. Sensors for digital imaging (area-scan camers)
3. Lens and their properties
4. Lighting techniques for machine vision
5. Optic filters and their application in computer vision systems
6. Line-scan cameras
7. Digital image representation, digital image enhancement
8. Image filtering, edge detection, feature extraction
9. Segmentation
10. Object recognition
11. Object classification
12. Object tracking
13. Lidar

Laboratory exercise

9 hod., compulsory

Teacher / Lecturer

doc. Ing. Pavel Škrabánek, Ph.D.

Syllabus

1. Introduction to MATLAB – computer vision toolbox.
2.Industrial cameras and their configuration.
3. Selection, installation and setting of lenses, lens defects.
4. Installation and manipulation with lighting. Impact of lighting on displaying of interest areas.
5. Impact of lighting on displaying of interest areas
6. Selection and implementation of filters. Impact of filters on displaying of interest areas.
7. Image enhancement using software tools.
8. Design and implementation of computer vision systems for a given task.
9 .Design and implementation of computer vision systems for a given task.
10. Design and implementation of computer vision systems for a given task.
11. Work with Lidar.
12. Individual project.
13. Individual project.

Guided consultation

34 hod., optionally

Teacher / Lecturer

prof. RNDr. Ing. Jiří Šťastný, CSc.

Syllabus

1.Basic principles of digital imaging
2. Sensors for digital imaging (area-scan camers)
3. Lens and their properties
4. Lighting techniques for machine vision
5. Optic filters and their application in computer vision systems
6. Line-scan cameras
7. Digital image representation, digital image enhancement
8. Image filtering, edge detection, feature extraction
9. Segmentation
10. Object recognition
11. Object classification
12. Object tracking
13. Lidar

Elearning

eLearning: currently opened course