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Fundamentals of Image Processing

* Exchange students do not have to consider this information when selecting suitable courses for an exchange stay.

Course Unit Code460-2070/01
Number of ECTS Credits Allocated4 ECTS credits
Type of Course Unit *Optional
Level of Course Unit *First Cycle
Year of Study *Third Year
Semester when the Course Unit is deliveredSummer Semester
Mode of DeliveryFace-to-face
Language of InstructionCzech
Prerequisites and Co-Requisites
PrerequisitiesCourse Unit CodeCourse Unit Title
460-2060Scripting Languages
Name of Lecturer(s)Personal IDName
FUS032Ing. Radovan Fusek, Ph.D.
Summary
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
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
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
Required Reading:
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:
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
Lectures, Tutorials
Assesment methods and criteria
Task TitleTask TypeMaximum Number of Points
(Act. for Subtasks)
Minimum Number of Points for Task Passing
Credit and ExaminationCredit and Examination100 (100)51
        CreditCredit45 20
        ExaminationExamination55 6