Lehrstuhl für Informatik 5 (Mustererkennung)


   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)

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)

Ait4Surgery: Automatic Intraoperative Tracking for Workflow and Dose Monitoring in X-Ray-based Minimally Invasive Surgeries
Prof. Dr. Björn Eskofier; Prof. Dr.-Ing. Andreas Maier
(01/06/2018 - 31/05/2021)

Human Motion: Biomechanical Simulation for the Reconstruction and Synthesis of Human Motion
Prof. Dr.-Ing. Andreas Maier; Prof. Dr. Björn Eskofier
(01/01/2017 - 31/12/2019)

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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Bopp, J., Ludwig, V., Seifert, M., Pelzer, G., Maier, A., Anton, G., & Riess, C. (2019). Simulation study on X-ray phase contrast imaging with dual-phase gratings. International Journal of Computer Assisted Radiology and Surgery, 14(1), 3-10. https://dx.doi.org/10.1007/s11548-018-1872-x
Minakaki, G., Canneva, F., Chevessier, F., Bode, F., Menges, S., Timotius, I.,... Klucken, J. (2019). Treadmill exercise intervention improves gait and postural control in alpha-synuclein mouse models without inducing cerebral autophagy. Behavioural Brain Research, 363, 199-215. https://dx.doi.org/10.1016/j.bbr.2018.11.035
Mullan, P., Rieß, C., & Freiling, F. (2019). Forensic Source Identification using JPEG Image Headers: The Case of Smartphones. In Digital Investigation. Oslo, NO.
Felsner, L., Hu, S., Ludwig, V., Anton, G., Maier, A., & Rieß, C. (2019). On the Characteristics of Helical 3-D X-ray Dark-field Imaging. (pp. 264-269). Lübeck.
Stromer, D., Christlein, V., Huang, X., Zippert, P., Hausotte, T., & Maier, A. (2019). Virtual cleaning and unwrapping of non-invasively digitized soiled bamboo scrolls. Scientific Reports, 9. https://dx.doi.org/10.1038/s41598-019-39447-0
Aubreville, M., Bertram, C.A., Klopfleisch, R., & Maier, A. (2019). Field of Interest Proposal for Augmented Mitotic Cell Count: Comparison of Two Convolutional Networks. In SciTePress (Eds.), Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING (pp. 30-37). Prague, CZ: SciTePress.
Goncalves, M., Aubreville, M., Mueller, S.K., Sievert, M., Maier, A., Iro, H., & Bohr, C. (2019). Probe-based confocal laser endomicroscopy in detecting malignant lesions of vocal folds. Acta Otorhinolaryngologica Italica. https://dx.doi.org/10.14639/0392-100X-2121
Aubreville, M., Stöve, M., Oetter, N., Goncalves, M., Knipfer, C., Neumann, H.,... Maier, A. (2019). Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images. International Journal of Computer Assisted Radiology and Surgery, 14(1), 31-42. https://dx.doi.org/10.1007/s11548-018-1836-1
Adams Seewald, L., Facco Rodrigues, V., Ollenschläger, M., Stoffel Antunes, R., Andre da Costa, C., da Rosa Righi, R.,... Fahrig, R. (2019). Toward analyzing mutual interference on infrared-enabled depth cameras. Computer Vision and Image Understanding. https://dx.doi.org/10.1016/j.cviu.2018.09.010
Magdalena, H., Luckner, C., Wicklein, J., Grunz, J.-P., Gassenmaier, T., Ritschl, L., & Kappler, S. (2019). Misalignment compensation for ultra-high-resolution and fast CBCT acquisitions. In Medical Imaging 2019: Physics of Medical Imaging (pp. 109481M). San Diego.
Luckner, C., Magdalena, H., Ritschl, L., Maier, A., & Kappler, S. (2019). Assessment of measurement deviations: length-extended x-ray imaging for orthopedic applications. In Medical Imaging 2019: Physics of Medical Imaging (pp. 1094839). San Diego, CA, USA.
Li, Q., Luckner, C., Hertel, M., Radicke, M., & Maier, A. (2019). Combining Ultrasound and X-Ray Imaging for Mammography. In Bildverarbeitung für die Medizin 2019. (pp. 245--250). Springer.
Hu, S., Felsner, L., Maier, A., Ludwig, V., Anton, G., & Riess, C. (2018). A 3-D Projection Model for X-ray Dark-field Imaging.
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

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