460-4129/01 – Image Processing in Automobiles (ZODA)

Gurantor departmentDepartment of Computer ScienceCredits4
Subject guarantordoc. Dr. Ing. Eduard SojkaSubject version guarantordoc. Dr. Ing. Eduard Sojka
Study levelundergraduate or graduateRequirementCompulsory
Study languageCzech
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFEIIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
HOL570 Ing. Michael Holuša, Ph.D.
SOJ10 doc. Dr. Ing. Eduard Sojka
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2

Subject aims expressed by acquired skills and competences

The course acquaints the students with the methods of digital image processing and image analysis. These methods are applied in the algorithms for autonomous driving. After passing the course, the student will understand the principles of the operations with the images and will be able to implement them.

Teaching methods



The following topics are covered: point and geometric operations, convolution, edge detection, feature extraction, classification methods, image segmentation, scene reconstruction, depth data analysis. The course includes the computer labs in which the computer programs are realized corresponding to the mentioned topics.

Compulsory literature:

1. Gonzalez, R., C., Woods, R., E.: Digital Image Processing, Prentice Hall, ISBN-10: 013168728X, ISBN-13: 978-0131687288, 2007 2. Burger, W., Burge, M., J.: Principles of Digital Image Processing: Fundamental Techniques, Springer, ISBN-10: 1848001908, ISBN-13: 978-1848001909, 2011

Recommended literature:

1. Petrou, M., Petrou, C.: Image Processing: The Fundamentals, Wiley, ISBN-10: 047074586X, ISBN-13: 978-0470745861, 2010 2. Brahmbhatt, S.: Practical OpenCV (Technology in Action), Apress, ISBN-10: 1430260793, ISBN-13: 978-1430260790, 2013

Way of continuous check of knowledge in the course of semester

Credit: 40 points (tasks assigned during the exercises) Exam: 60 points (written and oral form)


Other requirements

No further requirements are imposed on student.


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

Lectures: 1. Digital image. Sensors, transformation to the digital form. 2. Transformations of brightness. Geometric transformations. 3. Convolution and image filtration. 4. Fourier transform and its application in images. 5. Image compression. 6. Edge detection, corner detection, morphological image processing. 7. Segmentation methods. 8. Advanced segmentation methods. 9. Selection and computation of features for pattern recognition. 10. Classification methods. 11. Analysis of time-varying images. Object tracking. 12. Scene reconstruction from pair of images. Camera calibration. 13. Depth data processing. Object registration. 14. Spare space. Exercises: 1. Introduction to OpenCV. 2. Gamma correction, histogram equalization. 3. Convolution, convolution masks, image denoising. 4. Discrete Fourier Transform. 5. Morphological image processing. Erosion and dilatation operations. 6. Edge detection. 7. Image segmentation, thresholding, adaptive thresholding. 8. Distance-based image segmentation. 9. Classification methods. Computing moments. 10. Classification using an artificial neural network. 11. Tracking of objects in video sequences. Kalman filtering. 12. Object registration in depth data. 13. Spare space. 14. Credit.

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 40  20
        Examination Examination 60  20
Mandatory attendence parzicipation: Exercises: 50%

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2020/2021 (N0716A060001) Automotive Electronic Systems SPA P Czech Ostrava 1 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner