Machine Vision (FSI-VSV-K)

Academic year 2022/2023
Supervisor: prof. RNDr. Ing. Jiří Šťastný, CSc.  
Supervising institute: ÚAI all courses guaranted by this institute
Teaching language: Czech
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. 
Learning outcomes and competences:

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.
Course contents:
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.
Teaching methods and criteria:
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. 
Controlled participation in lessons:
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.
Type of course unit:
    Guided consultation in combined form of studies  1 × 9 hrs. compulsory                  
    Guided consultation  1 × 34 hrs. optionally                  
    Laboratory exercise  1 × 9 hrs. compulsory                  
Course curriculum:
    Guided consultation in combined form of studies 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
    Guided consultation 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 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.
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-K combined study --- no specialisation -- Cr,Ex 5 Compulsory 2 2 W