Module RO5100-KP12

Duration

2 Semester

Turnus of offer

each year, can be started in winter or summer semester

Credit points

12

Course of studies, specific fields and terms:

• Master Robotics and Autonomous Systems 2019, advanced module, advanced curriculum

Classes and lectures:

• Medical Robotics (exercise, 1 SWS)
• Seminar Robotics und Automation (seminar, 2 SWS)
• Medical Robotics (lecture, 2 SWS)
• Inverse Problems in Image Processing (exercise, 1 SWS)
• Inverse Problems in Image Processing (lecture, 2 SWS)

Workload:

• 190 hours private studies
• 150 hours in-classroom work
• 20 hours exam preparation

Contents of teaching:

• Introduction to inverse and ill-posed problems on the basis of selected examples (including seismology, impedance tomography, heat conduction, computed tomography, acoustics)
• Concept of ill-posedness of the inverse problem (Hadamard)
• Singular value decomposition and generalized inverse
• Regularization methods (eg Tikhonov, Phillips, Ivanov)
• Deconvolution
• Image restoration (deblurring, defocusing)
• Statistical methods (Bayes, maximum likelihood)
• Computed Tomography, Magnetic Particle Imaging

Qualification-goals/Competencies:

• Students are able to explain the concept of ill-posedness of the inverse problem and distinguish given inverse problems regarding good or bad posedness.
• They are able to formulate inverse problems of mathematical imaging and solve (approximate) with suitable numerical methods.
• They can assess the condition of a problem and the stability of a method.
• They master different regularization methods and are able to apply them to practical problems.
• They know methods to determine a suitable regularization.
• They can use methods of image reconstruction and restoration on real measurement data.
• Students are able to explain the concepts of forward and inverse kinematics for the examples of 3-joint and 6-joint robots.
• They are able to apply methods of medical robot systems and to simple practical applications.
• Students are able to transfer methods of motion learning to simple practical problems.
• Students are able to modify templates for dynamic calculations in order to create the calculations for their own constructions.

Grading through:

• Written or oral exam as announced by the examiner

Responsible for this module:

• Prof. Dr.-Ing. Achim Schweikard

Teacher:

• Institute of Computer Engineering
• Institute for Electrical Engineering in Medicine
• Institute of Medical Engineering
• Institute of Medical Informatics
• Institute for Robotics and Cognitive Systems

Literature:

• Kak and Slaney : Principles of Computerized Tomographic Imaging SIAM Series 33, New York, 2001
• Natterer and Wübbeling : Mathematical Methods in Image Reconstruction SIAM Monographs, New York 2001
• Bertero and Boccacci : Inverse Problems in Imaging IoP Press, London, 2002
• Andreas Rieder : Keine Probleme mit inversen Problemen Vieweg, Wiesbaden, 2003
• Buzug : Computed Tomography Springer, Berlin, 2008
• J. -C. Latombe : Robot Motion Planning Dordrecht: Kluwer 1990
• J.J. Craig : Introduction to Robotics Pearson Prentice Hall 2002
• : Vorlesungsskript: Med. Robotics

Language:

• offered only in English

Notes:

Last Updated:

26.07.2023