Lehrstuhl für Informatik 5 (Mustererkennung)


Description:


   Researchers and students at Pattern Recognition Lab (LME) work on the development and implementation of algorithms to classify and analyze patterns like images or speech. The research is mostly interdisciplinary and is focussed on medical- and health engineering. The LME has close national and international collaborations with other universities, research institutes and industrial partners.



A summary of the projects at the Pattern Recognition Lab is available for download as a comprehensive brochure (PDF)


Address:
Martensstraße 3
91058 Erlangen



Subordinate Organisational Units

Juniorprofessur für Medizinische Bildverarbeitung
Professur für Informatik (Mustererkennung)
Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports) - Reallocation / Closing


Research Fields

Big Data Applications
Medical Image Processing
Pattern Recognition & Machine Learning
Speech Processing and Understanding


Related Project(s)


DISPARITY: Digital, Semantic and Physical Analysis of Media Integrity
Prof. Dr.-Ing. Andreas Maier; Dr.-Ing. Christian Riess
(24/05/2016 - 23/05/2017)


(GRK 1773: Heterogene Bildsysteme):
RTG 1773: Heterogeneous Image Systems, Project C1
Prof. Dr. Rebecca Fahrig; Prof. Dr.-Ing. Andreas Maier
(01/10/2012 - 31/03/2017)


Open Access Publishing
Prof. Dr.-Ing. Joachim Hornegger
(01/01/2010 - 31/12/2019)



Publications (Download BibTeX)

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Hu, S., Felsner, L., Maier, A., Ludwig, V., Anton, G., & Riess, C. (2018). A 3-D Projection Model for X-ray Dark-field Imaging.
Bopp, J., Ludwig, V., Seifert, M., Pelzer, G., Maier, A., Anton, G., & Riess, C. (2018). Simulation study on X-ray phase contrast imaging with dual-phase gratings. International Journal of Computer Assisted Radiology and Surgery. https://dx.doi.org/10.1007/s11548-018-1872-x
Maier, A., Syben, C., Lasser, T., & Riess, C. (2018). A Gentle Introduction to Deep Learning in Medical Image Processing.
Felsner, L., Berger, M., Käppler, S., Bopp, J., Ludwig, V., Weber, T.,... Riess, C. (2018). Phase-Sensitive Region-of-Interest Computed Tomography. In 21st International Conference on Medical Image Computing and Computer Assisted Intervention (pp. 137-144). Granada, ES: Springer Verlag.
Köhler, G.T., Bätz, M., Naderi Bodaji, F., Kaup, A., Maier, A., & Riess, C. (2018). Bridging the Simulated-to-Real Gap: Benchmarking Super-Resolution on Real Data.
Bopp, J., Felsner, L., Hu, S., Käppler, S., & Riess, C. (2018). X-ray Phase Contrast: Research on a Future Imaging Modality. In Medical Imaging Systems - An Introductory Guide. (pp. 191--205).
Deitsch, S., Christlein, V., Berger, S., Buerhop-Lutz, C., Maier, A., Gallwitz, F.,... Rieß, C. (2018). Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images.
Davari, A., Özkan, H.C., Maier, A., & Riess, C. (2018). Fast Sample Generation with Variational Bayesian for Limited Data Hyperspectral Image Classification. In IEEE International Geoscience and Remote Sensing Symposium. Valencia, ES: IEEE.
Davari, A., Aptoula, E., Yanikoglu, B., Maier, A., & Riess, C. (2018). GMM-based Synthetic Samples for Classification of Hyperspectral Images with Limited Training Data. IEEE Geoscience and Remote Sensing Letters, 15(6), 942-946. https://dx.doi.org/10.1109/LGRS.2018.2817361
Deitsch, S., Buerhop-Lutz, C., Maier, A., Gallwitz, F., & Rieß, C. (2018). Segmentation of Photovoltaic Module Cells in Electroluminescence Images. arXiv.
Käppler, S., Maier, A., & Rieß, C. (2018). Differential Tomography: Influence of Sensitivity Direction and Noise-suppressing Windows. In Proceedings of the 5th International Conference on Image Formation in X-ray Computed Tomography (CT-Meeting) (pp. 119-122).
Timotius, I., Canneva, F., Minakaki, G., Pasluosta, C.F., Moceri, S., Casadei, N.,... Eskofier, B. (2018). Dynamic footprints of α-synucleinopathic mice recorded by CatWalk gait analysis. Data in Brief, 17, 189-193. https://dx.doi.org/10.1016/j.dib.2017.12.067
Schirrmacher, F., Köhler, G.T., & Rieß, C. (2018). Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging Problems.
Bopp, J., Ludwig, V., Gallersdörfer, M., Seifert, M., Pelzer, G., Maier, A.,... Riess, C. (2018). Towards a dual phase grating interferometer on clinical hardware. (pp. 1057321). SPIE.
Bopp, J., Gallersdörfer, M., Ludwig, V., Seifert, M., Maier, A., Anton, G., & Riess, C. (2018). Phasenkontrast Röntgen mit 2 Phasengittern und medizinisch relevanten Detektoren. In Proceedings des Workshops Bildverarbeitung für die Medizin 2018 - Algorithmen - Systeme - Anwendungen (pp. 170--175). Erlangen.
Ploner, S., Riess, C., Schottenhamml, J., Moult, E.M., Waheed, N.K., Fujimoto, J.G., & Maier, A. (2018). A Joint Probabilistic Model for Speckle Variance, Amplitude Decorrelation and Interframe Variance (IFV) Optical Coherence Tomography Angiography. In Proceedings des Workshops Bildverarbeitung für die Medizin 2018 - Algorithmen - Systeme - Anwendungen (pp. 98--102). Erlangen.
Rybakov, O., Stromer, D., Mischewski, I., & Maier, A. (2018). Segmentation of Fat and Fascias in Canine Ultrasound Images. (pp. 6). Hörsäle Medizin Kleiner Hörsaal Ulmenweg 18 91054 Erlangen, DE: Berlin, Heidelberg: Springer Vieweg.
Timotius, I., Canneva, F., Minakaki, G., Pasluosta, C.F., Moceri, S., Casadei, N.,... Eskofier, B. (2018). Dynamic footprint based locomotion sway assessment in α-synucleinopathic mice using Fast Fourier Transform and Low Pass Filter. Journal of Neuroscience Methods, 296, 1-11. https://dx.doi.org/10.1016/j.jneumeth.2017.12.004
Aubreville, M., Goncalves, M., Knipfer, C., Oetter, N., Würfl, T., Neumann, H.,... Maier, A. (2018). Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images - A Cross-Site Robustness Assessment. In Sheldon Wiebe, Hugo Gamboa,Ana Fred, Sergi Bermúdez i Badia (Eds.), Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) (pp. 27-34). Funchal, Madeira, Portugal, PT: SCITEPRESS – Science and Technology Publications, Lda.
Fürsattel, P., Placht, S., Maier, A., & Riess, C. (2018). Geometric primitive refinement for structured light cameras. Machine Vision and Applications, 29(2), 313-327. https://dx.doi.org/10.1007/s00138-017-0901-z

Last updated on 2018-12-01 at 15:08