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Seminar Inverse Problems in Image Processing and Computer Vision (SemInvProb)- Lecturers
- Prof. Dr.-Ing. habil. Andreas Maier, Prof. Dr.-Ing. Joachim Hornegger, Thomas Köhler, M. Sc.
- Details
- Seminar
4 cred.h, benoteter certificate, ECTS studies, ECTS credits: 5
nur Fachstudium, Sprache Englisch
Time and place: Mon 8:00 - 10:00, KH 1.021; comments on time and place: findet im Kollegienhaus statt
- Fields of study
- WPF INF-MA ab 1 (ECTS-Credits: 5)
WPF MT-MA ab 1
WPF CE-MA-SEM ab 1
- Prerequisites / Organisational information
- Strong mathematical background and basic knowledge of image processing and/or computer vision is desirable.
- Contents
- An inverse problem refers to the inference of latent model parameters from a set of noisy measurements under a generative model that explains the causal relationship between both. Despite the broad field of applications, this typically leads to optimization problems that can be tackled by common mathematical tools. In computer vision and image processing, many tasks of practical relevance can be formulated as inverse problems ranging from low-level vision to image analysis and scene understanding.
This seminar focuses on theory along with some of the classical applications of inverse problems in image processing and computer vision. The topics covered by the seminar include but are not limited to:
image filtering and denoising
blind image restoration, upsampling and super-resolution
motion estimation
inpainting
image segmentation
shape-from-X and 3-D reconstruction
- ECTS information:
- Credits: 5
- Additional information
- Keywords: inverse problems, ill-posed problems, regularization, optimization, image processing, computer vision
Expected participants: 10, Maximale Teilnehmerzahl: 12
www: https://www5.cs.fau.de/lectures/ws-1617/seminar-inverse-problems-in-image-processing-and-computer-vision-seminvprob/
- Verwendung in folgenden UnivIS-Modulen
- Startsemester WS 2016/2017:
- Seminar Inverse Problems in Image Processing and Computer Vision (SemInvProb)
- Department: Chair of Computer Science 5 (Pattern Recognition)
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