Course detail
Machine Vision
FSI-VSV-A Acad. year: 2019/2020 Winter semester
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
Supervisor
Department
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.
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.
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.
The study programmes with the given course
Programme M2I-A: Mechanical Engineering, Master's
branch M-AIŘ: Applied Computer Science and Control, compulsory
Type of course unit
Lecture
26 hours, 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. 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 hours, 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.