A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.
BI-SVZ.21 Machine vision and image processing Extent of teaching: 2P+2C Instructor: Brchl L., Jiřina M., Novák J. Completion: Z,ZK Department: 18105 Credits: 5 Semester: L,Z Annotation:
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.
Lecture syllabus:
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 Seminar syllabus:
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 Literature:
[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 Requirements:
https://courses.fit.cvut.cz/BI-SVZ/classification/index.html
The course is also part of the following Study plans:
Page updated 27. 4. 2024, semester: L/2022-3, L/2020-1, Z,L/2023-4, Z/2020-1, Z,L/2019-20, Z/2022-3, Z,L/2021-2, Z/2024-5, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška