19M033SOS - Modern Image Processing Systems

Course specification
Course title		Modern Image Processing Systems
Acronym		19M033SOS
Study programme		Electrical Engineering and Computing
Module		Applied Mathematics, Audio and Video Technologies, Biomedical and Environmental Engineering, Biomedical and Nuclear Engineering, Computer Engineering and Informatics, Electronics and Digital Systems, Energy Efficiency, Information and Communication Technologies, Microwave Engineering, Nanoelectronics and Photonics, Power Systems - Networks and Systems, Power Systems - Renewable Energy Sources, Power Systems - Substations and Power Equipment, Signals and Systems, Software Engineering
Type of study		master academic studies
Lecturer (for classes)		professor PhD Ana Gavrovska
Lecturer/Associate (for practice)		professor PhD Ana Gavrovska
Lecturer/Associate (for OTC)		professor PhD Ana Gavrovska
ESPB		6.0	Status	elective
Condition		None
The goal		Introducing students to modern system components and concepts of digital image/picture processing.
The outcome		Empowering the students to use known methods for digital image processing and to create and develop new practical algorithms, as well as computer codes for the processing.
Contents
Contents of lectures		Sensors and image acquisition in modern systems. Basic and advanced image processing in spatial and transform domains. Features. AI-based methods for segmentation and classification in images. Compression principles, transmission, and image quality. Satellite, medical, and multispectral image processing systems. Decision-making and machine learning in image processing. Smart system applications.
Contents of exercises		Auditory exercises following the lectures. Exercises in a computer lab where the processing is performed using libraries and tools for image processing and computer vision.
Literature
R. Gonzales, R. Woods, Digital Image Processing, 4th Ed., Prentice Hall, 2018. R. Gonzales, R. Woods, S. Eddins, Digital Image Processing using Matlab, Gatesmark Publishing, 3rd Ed., 2020. W. Burger, M. J. Burge, Digital image processing: an introduction using Java, Springer, 2016. R. Szeliski, Computer vision: algorithms and applications, Springer Science & Business Media, 2010. R. Chityala and S. Pudipeddi, Image processing and acquisition using Python, CRC Press, 2014.
Number of hours per week during the semester/trimester/year
Lectures	Exercises	OTC	Study and Research	Other classes
3	1	1
Methods of teaching		Lectures, exercises and student assignments.
Knowledge score (maximum points 100)
Pre obligations		Points	Final exam	Points
Activites during lectures		0	Test paper	30
Practical lessons		20	Oral examination	0
Projects		50
Colloquia		0
Seminars		0