19M041DOS2 - Digital Image Processing 2

Course specification
Course title		Digital Image Processing 2
Acronym		19M041DOS2
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)		asst. professor PhD Dragomir El Mezeni
Lecturer/Associate (for practice)		asst. professor PhD Dragomir El Mezeni
Lecturer/Associate (for OTC)		asst. professor PhD Dragomir El Mezeni
ESPB		6.0	Status	elective
Condition		Passed exam in Digital Image Processing from the bachelor academic studies.
The goal		The objective of the course is to introduce advanced topics from digital image processing such as feature extraction and description, image classification, image analysis, object recognition and analysis of video signal. This course represents algorithmic background for development of machine vision based embedded and industrial systems.
The outcome		After completing this course, students will be familiar with theoretical and practical aspects of most important algorithms in high level digital image processing and image analysis. Students will be able to develop solutions for different digital image processing problems regarding image analysis, object recognition used in embedded and industrial systems.
Contents
Contents of lectures		Feature extractions and description. Feature matching. Geometric image transformations. Image registration. Optical flow. Basic concepts of pattern recognition. Image classification. Image clustering and segmentation. Introduction to artificial neural networks with applications in image analysis. Convolutional neural networks. Video signal analysis and object tracking. Scene content analysis.
Contents of exercises		Exercises in computer laboratory are used for practical implementation of algorithms introduced during lectures. Students are using these techniques to solve specific tasks for the homework assignments. For the final project at the end of the course students should develop complete solution for some practical problem.
Literature
R. Szeliski, Computer Vision: Algorithms and Applications, Springer, 2010 (Original title) R. Klette, Concise computer vision, Springer, 2014 (Original title) D. Forsyth and J. Ponce, Computer Vision: A Moden Approach, Pearson, 2011 (Original title) I. Goodfellow et al, Deep Learning, The MIT Press, 2016 (Original title)
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 in computer laboratory. Homework assignments. Final project.
Knowledge score (maximum points 100)
Pre obligations		Points	Final exam	Points
Activites during lectures			Test paper
Practical lessons		70	Oral examination	30
Projects
Colloquia
Seminars