| Course Unit Code | 460-4129/01 |
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| Number of ECTS Credits Allocated | 4 ECTS credits |
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| Type of Course Unit * | Compulsory |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | First Year |
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| Semester when the Course Unit is delivered | Winter Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | Czech |
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| Prerequisites and Co-Requisites | There are no prerequisites or co-requisites for this course unit |
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| Name of Lecturer(s) | Personal ID | Name |
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| SOJ10 | doc. Dr. Ing. Eduard Sojka |
| HOL570 | Ing. Michael Holuša, Ph.D. |
| Summary |
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| 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. |
| Learning Outcomes of the Course Unit |
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| 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. |
| Course Contents |
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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. |
| Recommended or Required Reading |
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| Required Reading: |
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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 |
1. Sojka, E., Gaura, J., Krumnikl, M.: Matematické základy digitálního zpracování obrazu, VŠB-TU Ostrava, 2011
2. Sojka, E.: Digitální zpracování a analýza obrazů, učební texty, VŠB-TU Ostrava, 2000 (ISBN 80-7078-746-5)
3. Gonzalez, R., C., Woods, R., E.: Digital Image Processing, Prentice Hall, ISBN-10: 013168728X, ISBN-13: 978-0131687288, 2007 |
| Recommended Reading: |
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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 |
1. Burger, W., Burge, M., J.: Principles of Digital Image Processing: Fundamental Techniques, Springer, ISBN-10: 1848001908, ISBN-13: 978-1848001909, 2011
2. Petrou, M., Petrou, C.: Image Processing: The Fundamentals, Wiley, ISBN-10: 047074586X, ISBN-13: 978-0470745861, 2010
3. Brahmbhatt, S.: Practical OpenCV (Technology in Action), Apress, ISBN-10: 1430260793, ISBN-13: 978-1430260790, 2013 |
| Planned learning activities and teaching methods |
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| Lectures, Tutorials |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Credit and Examination | Credit and Examination | 100 (100) | 51 |
| Credit | Credit | 40 | 20 |
| Examination | Examination | 60 | 20 |