Logo ČVUT
Loading...
CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2025/2026
Faculties | School elective courses | ECTS courses catalogue | Settings | Česká verze
Welcome page > Faculty of Information Technology > Department of Applied Mathematics > Machine vision and image processing

Machine vision and image processing

Display time-table
Code Completion Credits Range Language
BI-SVZ.21 Z,ZK 5 2P+2C Czech
Course guarantor:
Marcel Jiřina
Lecturer:
Marcel Jiřina, David Kramný, Jakub Novák
Tutor:
Justýna Frommová, Marcel Jiřina, David Kramný, Jakub Novák
Supervisor:
Department of Applied Mathematics
Synopsis:

Camera systems are becoming a common part of life by being universally available. Related to this phenomenon is the need to process and evaluate image information. The course introduces students to different types of camera systems and a variety of methods for image and video processing. The course is focused on practical use of camera systems for solving problems of practice that the graduates may encounter.

Requirements:

https://courses.fit.cvut.cz/BI-SVZ/classification/index.html

Syllabus of lectures:

1. Machine Vision and Physical Principles

2. Types of Sensors and Optics

3. Camera System and Image Processing

4. Image as a Matrix

5. Perspective and Image Geometry

6. Image Preprocessing - Transformation and Correction

7. Image Preprocessing - Morphology and Shape Characteristics

8. Image Preprocessing - Spatial and Frequency Domain Filtering

9. Image Segmentation - Edge Detection

10. Image Segmentation - Hough Transform and Region-based Segmentation

11. Image Recognition, Object Detection, Modern Trends

12. Modern Trends in Image Recognition

Syllabus of tutorials:

1. Introduction to tools

2. Working with cameras and basics of image processing

3. Optics defects, camera calibration

4. Image segmentation

5. Utilizing lights

6. Perspective of images

7. Working with depth cameras

8. Line-scan cameras

9. Transformation techniques

10. Image perspective, 360° lenses

11. Basics of measurement with a thermal camera

12. Image classification, object detection

Study Objective:

The student is expected to gain the following from the course:

- The ability to respond to vaguely defined demands for machine vision in practice.

- Knowledge that allows them to understand problems from a machine vision perspective and design an appropriate imaging system, including the camera, lens, and lighting, with the aim of obtaining ideal image data for further processing.

- Theoretical knowledge about a range of algorithms that can be applied with minimal effort to the acquired ideal data to solve tasks.

- Practical skills in implementing these algorithms using Python on real-world data.

Study materials:

[1] McAndrew A., Computational Introduction to Digital Image Processing, CRC Press, 2. vydání, 2016

[2] Sundararajan D., Digital Image Processing: A Signal Processing and Algorithmic Approach, Springer, 2017

[3] Birchfield S., Image Processing and Analysis, Cengage Learning, 2016

[4] Acharya T., Ray A. K., Image Processing: Principles and Applications, Wiley, 2005

[5] Burger W., Burge M. J., Principles of Digital Image Processing: Fundamental Techniques, Springer-Verlag, 2009

Note:

The course is presented in Czech.

Further information:
https://courses.fit.cvut.cz/BI-SVZ/
Time-table for winter semester 2025/2026:
Time-table is not available yet
Time-table for summer semester 2025/2026:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2025-05-19
Copyright © Jiří Kosek, 2010–2022 | Copyright © ČVUT, 2010–2022
Hosted by Výpočetní a informační centrum ČVUT
Send your questions and comments to HelpDesk ČVUT or to spravcekos@cvut.cz
For updated information see http://bilakniha.cvut.cz/en/predmet6703906.html