Course detail
Machine Vision
FSI-VSVAcad. year: 2020/2021
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
Learning outcomes of the course unit
Understanding of basic principles of digital image capturing and processing. Ability to analysis real world problems, to select appropriate hardware for this problem, and to design and implement adequate software.
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
In order to be awarded the course-unit credit, students must prove 100 % active participation in laboratory exercises. The exam is oral where student compiles two main themes which were presented during the lectures.
Course curriculum
Not applicable.
Work placements
Not applicable.
Aims
The goal of the course is understanding of the principles of digital image capturing and processing by students, within the context of industrial and scientific 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/ (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 (EN)
GONZALEZ, Rafael C. a Richard E. WOODS. Digital image processing. Fourth edition. New York: Pearson, [2018]. ISBN 978-0133356724. (EN)
HLAVÁČ, Václav a Miloš SEDLÁČEK. Zpracování signálů a obrazů. Fourth edition. Praha: Vydavatelství ČVUT, 2000. ISBN 80-010-2114-9. (CS)
MCMANAMOM, Paul. Field Guide to Lidar. 1. Bellingham, Washington 98227-0010 USA: SPIE, 2015. ISBN 9781628416541. (EN)
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 (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 (EN)
GONZALEZ, Rafael C. a Richard E. WOODS. Digital image processing. Fourth edition. New York: Pearson, [2018]. ISBN 978-0133356724. (EN)
HLAVÁČ, Václav a Miloš SEDLÁČEK. Zpracování signálů a obrazů. Fourth edition. Praha: Vydavatelství ČVUT, 2000. ISBN 80-010-2114-9. (CS)
MCMANAMOM, Paul. Field Guide to Lidar. 1. Bellingham, Washington 98227-0010 USA: SPIE, 2015. ISBN 9781628416541. (EN)
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 (EN)
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. (CS)
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. (CS)
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. (CS)
HAVEL, Otto. Strojové vidění IV: Osvětlovače. Automa. Automa – časopis pro automatizační techniku, s. r. o., 2008, 14(4), 47-49. ISSN 1210-9592. (CS)
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. (CS)
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. (CS)
HAVEL, Otto. Strojové vidění IV: Osvětlovače. Automa. Automa – časopis pro automatizační techniku, s. r. o., 2008, 14(4), 47-49. ISSN 1210-9592. (CS)
Elearning
eLearning: currently opened course
Classification of course in study plans
Type of course unit
Lecture
26 hod., optionally
Teacher / Lecturer
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
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
26 hod., compulsory
Teacher / Lecturer
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.
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.
Elearning
eLearning: currently opened course