Module CS3100-KP08, CS3100SJ14

Duration

1 Semester

Turnus of offer

each winter semester

Credit points

8

Course of studies, specific fields and terms:

• Master CLS 2023, compulsory, mathematics
• Bachelor Robotics and Autonomous Systems 2020 , compulsory, Robotics and Autonomous Systems
• Bachelor Computer Science 2019, optional subject, major subject informatics
• Bachelor Computer Science 2019, compulsory, Canonical Specialization Bioinformatics and Systems Biology
• Bachelor MES 2020, compulsory, computer science
• Bachelor Media Informatics 2020, optional subject, computer science
• Bachelor Medical Informatics 2019, optional subject, computer science
• Bachelor Computer Science 2014, compulsory, specialization field robotics and automation
• Bachelor Computer Science 2014, compulsory, specialization field bioinformatics
• Bachelor Computer Science 2016, compulsory, Canonical Specialization Bioinformatics
• Bachelor Computer Science 2016, optional subject, major subject informatics
• Bachelor Computer Science 2016, compulsory, Canonical Specialization Web and Data Science
• Master CLS 2016, compulsory, mathematics
• Bachelor Robotics and Autonomous Systems 2016, compulsory, Robotics and Autonomous Systems
• Bachelor IT-Security 2016, optional subject, computer science
• Bachelor Biophysics 2016, compulsory, computer science
• Bachelor Medical Informatics 2014, compulsory, computer science
• Bachelor MES 2014, compulsory, computer science
• Bachelor Media Informatics 2014, optional subject, computer science
• Bachelor Computer Science 2014, optional subject, central topics of computer science

Classes and lectures:

• Image Processing (lecture, 2 SWS)
• Image Processing (exercise, 1 SWS)
• Signal Processing (exercise, 1 SWS)
• Signal Processing (lecture, 2 SWS)

Workload:

• 90 hours in-classroom work
• 110 hours private studies
• 40 hours exam preparation

Contents of teaching:

• Linear time-invariant systems
• Impulse response
• Convolution
• Fourier transform
• Transfer function
• Correlation and energy density of deterministic signals
• Sampling
• Discrete-time signals and systems
• Discrete-time Fourier transform
• z-Transform
• FIR and IIR filters
• Block diagrams
• FIR filter design
• Discrete Fourier transform (DFT)
• Fast Fourier transform (FFT)
• Characterization and processing of random signals
• Introduction, interest of visual information
• 2D Sampling
• Image enhancement
• Edge detection
• Multiresolution concepts: Gaussian and Laplacian Pyramid, wavelets
• Principles of image compression
• Segmentation
• Morphological image processing
• Students work self-actingly and independently with regard to the roles of GSP of the University of Lübeck.

Qualification-goals/Competencies:

• Students are able to explain the fundamentals of linear system theory.
• They are able to define and competently explain the essential elements of signal processing mathematically.
• They will have a command of mathematical methods for the description and analysis of continuous-time and discrete-time signals and systems.
• They are able to design digital filters and know various structures for their implementation.
• They are able to explain the basic techniques for describing and processing of random signals.
• They will have basic knowledge of two-dimensional system theory.
• They are able to describe the main techniques for image analysis and image enhancement.
• They are able to apply the learned principles in practice.

Grading through:

• written exam

Responsible for this module:

• Prof. Dr.-Ing. Alfred Mertins

Teacher:

• Institute for Signal Processing
• Prof. Dr.-Ing. Alfred Mertins

Literature:

• A. Mertins : Signaltheorie: Grundlagen der Signalbeschreibung, Filterbänke, Wavelets, Zeit-Frequenz-Analyse, Parameter- und Signalschätzung Springer-Vieweg, 3. Auflage, 2013
• A. K. Jain : Fundamentals of Digital Image Processing Prentice Hall, 1989
• Rafael C. Gonzalez, Richard E. Woods : Digital Image Processing Prentice Hall 2003

Language:

• offered only in German

Notes:

Last Updated:

17.02.2022