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19E033SOS - Image Processing Systems

Course specification
Course title Image Processing Systems
Acronym 19E033SOS
Study programme Electrical Engineering and Computing
Module Information and Communication Technologies - Audio and Video Technologies, Information and Communication Technologies - Internet and Mobile Communications, Information and Communication Technologies - Microwave Technology, Telecommunications and Information Technologies - Audio and Video Technologies, Telecommunications and Information Technologies - Information and Communication Technologies, Telecommunications and Information Technologies - Microwave Technology
Type of study bachelor 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 basic 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 algorithms, as well as computer codes for the processing.
Contents
Contents of lectures Concepts of digital two-dimensional signal processing. Sensors and image/picture acquisition. Basic image processing in spatial and transform domains. Color image processing. Morphological operations. Segmentation, feature extraction, and object classification. Image compression principles. Picture archiving, transfer, and presentation. Quality estimation. Digital image processing 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
  1. R. Gonzales, R. Woods, Digital Image Processing, 4th Ed., Prentice Hall, 2018.
  2. R. Gonzales, R. Woods, S. Eddins, Digital Image Processing using Matlab, Gatesmark Publishing, 3rd Ed., 2020.
  3. W. Burger, M.J. Burge, Digital image processing: an introduction using Java, Springer, 2016.
  4. R. Szeliski, Computer vision: algorithms and applications, Springer Science & Business Media, 2010.
  5. 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 40
Practical lessons 20 Oral examination 0
Projects 40
Colloquia 0
Seminars 0