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19M033SOS - Modern Image Processing Systems

Course specification
Course title Modern Image Processing Systems
Acronym 19M033SOS
Study programme Electrical Engineering and Computing
Module
Type of study master academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
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 of 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
URL to the subject page https://www.etf.bg.ac.rs/fis/karton_predmeta/19M033SOS-2020
URL to lectures https://teams.microsoft.com/l/team/19%3aeQlcBO90W6KNaE0BARyKn9IStQWWdcZwxhrwKmR8tcs1%40thread.tacv2/conversations?groupId=60374604-dc3b-4278-8cad-f58d0b644566&tenantId=1774ef2e-9c62-478a-8d3a-fd2a495547ba
Contents of lectures Sensors and image acquisition in modern systems. Basic and advanced image processing in spatial and transform domain. Features. Modern methods for segmentation and classification in image. Compression principles, transmission and image quality. Satellite, medical, multispectral image processing systems. Decision making and machine learning in image processing. Image processing in smart systems.
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
  1. Rafael Gonzales, Richard Woods, Digital Image Processing, 3rd Ed., Prentice Hall, 2008.
  2. Rafael Gonzales, Richard Woods, Steven Eddins, Digital Image Processing using Matlab, Gatesmark Publishing, 2009.
  3. Wilhelm Burger, Mark J. Burge, Digital image processing: an introduction using Java, Springer, 2016.
  4. Richard Szeliski, Computer vision: algorithms and applications, Springer Science & Business Media, 2010.
  5. Ravishankar Chityala and Sridevi 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