Computer Vision and Image Processing
| Code | Completion | Credits | Range | Language |
|---|---|---|---|---|
| BI-SVZ | Z,ZK | 5 | 2P+2C | Czech |
- Lecturer:
- Marcel Jiřina (guarantor)
- Tutor:
- Jakub Novák, Marcel Jiřina (guarantor), Lukáš Brchl, Jakub Žitný
- Supervisor:
- Department of Applied Mathematics
- Synopsis:
-
Camera systems become a common part of life by being universally available. This phenomenon also relates to the need to process and evaluate image information. The subject acquaints students with different types of camera systems and with a variety of image and video processing methods. The course is focused on the use of camera systems for solving practical problems, which students can meet in a real life.
- Requirements:
-
Interest in image processing. Knowledge of data processing and mathematics is welcomed.
- Syllabus of lectures:
-
1. Introduction to image and video processing. Technical means for acquiring and recording images and video. An arrangement of the measuring workplace.
2. The principle of scanning, image digitization, storage, and transmission. Light conditions, optical filters, lenses. Types of cameras and their properties. Scanning in different frequency bands.
3. Loss and lossless image and video compression. Data formats.
4. Image preprocessing techniques, change of resolution, depth change, color scheme transfers.
5. Image sampling and quantization, Fourier transform, noise filtering in the spatial and frequency domain.
6. Image blur and focus, histogram and its equalization, geometric and luminosity transformations.
7. Convolutional methods of filtration and detection, detection of edges and significant parts in the image, Hough transformation
8. Image degradation due to motion, reconstruction by filters.
9. 2D and 3D segmentation methods, surface and edge segmentation, Watershed segmentation, image registration
10. Morphological operations, opening and closing operations
11. Video analysis, detection of a scene change
12. Extract descriptive characteristics of objects in the image. Symptoms of recognition and machine learning, classification and cluster analysis
13. Utilizing artificial intelligence methods for understanding the image.
- Syllabus of tutorials:
-
1. Requirements for assessment, assignment of semestral work. Working with the camera, its powering and data connection, a lighting of the scene, preparation of the measuring workplace.
2 Selection of a proper camera system for a given task, selection of a suitable optics, resolution of the camera, a positioning of the camera, determination of suitability of the camera system for the given task, selection of filters, selection of illumination and provision of lighting conditions.
3. Measurement using a black-and-white matrix camera for the purpose of checking the geometric dimensions of the object.
4. Measurement using color matrix camera, detection of different materials.
5. High-speed Camera Measurement - Sensing fast mechanical processes and motion analysis.
6. Line Camera Measurement - Continuous Motion Detection and Vulnerability Detection.
7. Measurement by thermal imaging camera - measurement and evaluation of thermal processes, measurement of the course of heat change.
8. Measurement with 3D Sensor - motion detection.
9. Multi-page measurement, multiple cameras communication - comprehensive quality control.
10. Selective sensing using band filters - sensing in near UV area.
11. Selective sensing using band filters - sensing in near IR area.
12. Presentation and defense of the semester work.
13. Assessment.
- Study Objective:
-
The aim of the study is to provide students with knowledge in the field of image and video acquisition and processing. The student should acquire knowledge about specific imaging techniques, ways to use them correctly for specific tasks, and learn various image processing techniques to extract the required information and knowledge from image data.
- Study materials:
-
[1] Šonka, M., Hlaváč V., Boyle R., Image processing, analysis, and machine vision. Cengage Learning, 2014.
[2] Bradski, G., Kaehler A., Learning OpenCV: Computer vision with the OpenCV library. „ O'Reilly Media, Inc.“, 2008.
[3] Pratt W. K., Digital Image Processing (3rd edition), John Wiley, New York, 2001
[4] Gonzales R. C., Woods R. E., Digital Image Processing (3rd edition), Prentice Hall, 2016
[5] Gonzales R. C. a kol., Digital Image Processing using MATLAB, Prentice Hall, 2004
- Note:
- Further information:
- https://courses.fit.cvut.cz/BI-SVZ/
- Time-table for winter semester 2018/2019:
-
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon Tue Fri Thu Fri - Time-table for summer semester 2018/2019:
-
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon Tue Fri Thu Fri - The course is a part of the following study plans:
-
- Bc. Programme Informatics, in Czech, Version 2015 to 2019 (elective course)
- Bc. Branch Security and Information Technology, in Czech, Version 2015 to 2019 (elective course)
- Bc. Branch Computer Science, in Czech, Version 2015 to 2019 (elective course)
- Bc. Branch Computer Engineering, in Czech, Version 2015 to 2019 (elective course)
- Bachelor Branch Information Systems and Management, in Czech, Version 2015 to 2019 (elective course)
- Bachelor Branch Knowledge Engineering, in Czech, Version 2015, 2016 and 2017 (elective course)
- Bachelor Branch WSI, Specialization Software Engineering, in Czech, Version 2015 to 2019 (elective course)
- Bachelor Branch, Specialization Web Engineering, in Czech, Version 2015 to 2019 (elective course)
- Bachelor Branch WSI, Specialization Computer Grafics, in Czech, Version 2015 to 2019 (elective course)
- Bachelor Branch Knowledge Engineering, in Czech, Version 2015 to 2019 (elective course)