460-2070/02 – Fundamentals of Image Processing (ZAO)
Gurantor department | Department of Computer Science | Credits | 4 |
Subject guarantor | Ing. Radovan Fusek, Ph.D. | Subject version guarantor | Ing. Radovan Fusek, Ph.D. |
Study level | undergraduate or graduate | Requirement | Optional |
Year | 3 | Semester | summer |
| | Study language | English |
Year of introduction | 2019/2020 | Year of cancellation | |
Intended for the faculties | FEI | Intended for study types | Bachelor |
Subject aims expressed by acquired skills and competences
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.
Teaching methods
Lectures
Tutorials
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.
Compulsory literature:
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
Recommended literature:
1. Michael Beyeler: Machine Learning for OpenCV, Packt Publishing, ISBN-13: 978-1783980284, 2017
Way of continuous check of knowledge in the course of semester
Conditions for granting the credit:
The tasks that form the program of exercises must be worked out.
Exam - written test.
E-learning
Other requirements
Without additional requirements.
Prerequisities
Co-requisities
Subject has no co-requisities.
Subject syllabus:
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
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
Předmět neobsahuje žádné hodnocení.