Course Unit Code | 460-2070/01 |
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Number of ECTS Credits Allocated | 4 ECTS credits |
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Type of Course Unit * | Optional |
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Level of Course Unit * | First Cycle |
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Year of Study * | Third Year |
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Semester when the Course Unit is delivered | Summer 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 | |
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| Prerequisities | Course Unit Code | Course Unit Title |
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| 460-2060 | Scripting Languages |
Name of Lecturer(s) | Personal ID | Name |
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| FUS032 | Ing. Radovan Fusek, Ph.D. |
Summary |
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The following topics will be discussed: Image analysis in self-driving cars, image analysis in Industry 4.0, detection and recognition of 2D and 3D objects, detection and recognition of people and objects in the security and spy industry. |
Learning Outcomes of the Course Unit |
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The course acquaints with the topics of image analysis, which accompany the people in everyday life. These topics are a natural part of development of the society with the transition towards Industry 4.0. In case of completing the course, students gain an overview of modern methods of image analysis. In the case of their deeper interest, the students can attend the master study courses that are focused on digital processing and image analysis in which the students will obtain deeper information. |
Course Contents |
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Lectures:
1. Introduction to actual topics in image analysis
2. Image-based driver behavior recognition
3. Analysis of objects in vehicle surroundings, the detection of vehicles, pedestrians, traffic signs, traffic lights, etc.
4. Analysis of data gained from the self-driving car sensors
5. Detection and recognition of 3D objects and its application in augmented reality
6. Depth data gathering and analysis
7. Object detection in aerial and satellite images, buildings detection, parking lot analysis, smart cities
8. Image-based people identification, biometry
9. Image and video editing with a goal to fake reality
10. Actual and future trends in artificial intelligence in image processing
Exercises:
1. Introduction to the image processing libraries
2. Practice the methods for image-based driver behavior recognition
3. Experiments with the methods for analysis of objects in vehicle surroundings
4. Introduction to the processing of car sensor data
5. Practice the methods for 3D object recognition
6. Experiments with the depth data
7. Experiments with the methods for the parking lot and satellite image analysis
8. Study of recognition techniques for image-based people identification
9. Experiments with image and video editing with a goal to fake reality
10. Introduction to the new trends in artificial intelligence in image processing |
Recommended or Required Reading |
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Required Reading: |
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1. Gary Bradski, Adrian Kaehler: Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library, O'Reilly Media, 2017
2. Petrou, M., Petrou, C.: Image Processing: The Fundamentals, Wiley, ISBN-10: 047074586X, ISBN-13: 978-0470745861, 2010 |
1. Gary Bradski, Adrian Kaehler: Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library, O'Reilly Media, 2017
2. Petrou, M., Petrou, C.: Image Processing: The Fundamentals, Wiley, ISBN-10: 047074586X, ISBN-13: 978-0470745861, 2010
3. E. Sojka: Digitální zpracování a analýza obrazů, učební texty, VŠB-TU Ostrava, ISBN 80-7078-746-5, 2000 |
Recommended Reading: |
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1. Michael Beyeler: Machine Learning for OpenCV, Packt Publishing, ISBN-13: 978-1783980284, 2017 |
1. Michael Beyeler: Machine Learning for OpenCV, Packt Publishing, ISBN-13: 978-1783980284, 2017 |
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 | 45 | 20 |
Examination | Examination | 55 | 6 |