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)

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(Advancing osteoporosis medicine by observing bone microstructure and remodelling using a fourdimensional nanoscope):
4-D nanoSCOPE: Advancing osteoporosis medicine by observing bone microstructure and remodelling using a fourdimensional nanoscope
Prof. Dr.-Ing. Andreas Maier
(01/04/2019 - 31/03/2025)

Time Machine: Big Data of the Past for the Future of Europe
Prof. Dr.-Ing. Andreas Maier
(01/03/2019 - 29/02/2020)

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)

Verbesserte Charakterisierung des Versagensverhaltens von Blechwerkstoffen durch den Einsatz von Mustererkennungsmethoden
Prof. Dr.-Ing. Andreas Maier
(01/04/2017 - 31/03/2019)

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

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
Huang, X., Yang, H., Huang, Y., Shi, L., He, F., Maier, A., & Yan, M. (2019). Robust mixed one-bit compressive sensing. Signal Processing, 162, 161-168. https://dx.doi.org/10.1016/j.sigpro.2019.04.011
Bayer, S., Zhai, Z., Strumia, M., Tong, X., Gao, Y., Staring, M.,... Ravikumar, N. (2019). Registration of vascular structures using a hybrid mixture model. International Journal of Computer Assisted Radiology and Surgery, 1–10. https://dx.doi.org/10.1007/s11548-019-02007
Deitsch, S., Christlein, V., Berger, S., Buerhop-Lutz, C., Maier, A., Gallwitz, F., & Riess, C. (2019). Automatic classification of defective photovoltaic module cells in electroluminescence images. Solar Energy, 185, 455-468. https://dx.doi.org/10.1016/j.solener.2019.02.067
Kling, L., Mill, L., Grüneboom, A., Sarau, G., Herrmann, M., Schett, G.,... Christiansen, S.H. (2019). THE 4-D+ NANOSCOPE: THE RESOLUTION FOR BIOLOGICAL SAMPLES. In ANNALS OF THE RHEUMATIC DISEASES (pp. 31-31). Madrid, ES: LONDON: BMJ PUBLISHING GROUP.
Felsner, L., Hu, S., Maier, A., Bopp, J., Ludwig, V., Anton, G., & Riess, C. (2019). A 3-D Projection Model for X-ray Dark-field Imaging. Scientific Reports, 9.
Yu, Z., Xiang, Q., Meng, J., Kou, C., Ren, Q., & Lu, Y. (2019). Retinal image synthesis from multiple-landmarks input with generative adversarial networks. Biomedical Engineering Online, 18. https://dx.doi.org/10.1186/s12938-019-0682-x
Facco Rodrigues, V., da Rosa Righi, R., Andre da Costa, C., Eskofier, B., & Maier, A. (2019). On Providing Multi-Level Quality of Service for Operating Rooms of the Future. Sensors, 19(10). https://dx.doi.org/10.3390/s19102303
Christlein, V. (2019). Handwriting Analysis with Focus on Writer Identification and Writer Retrieval (Dissertation).
Schröter, H., Nöth, E., Maier, A., Cheng, R., Barth, V., & Bergler, C. (2019). Segmentation, Classification, and Visualization of Orca Calls Using Deep Learning. In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 8231-8235). Brighton, GB: IEEE.
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
Maier, A., Syben, C., Lasser, T., & Riess, C. (2019). A gentle introduction to deep learning in medical image processing [Eine sanfte Einführung in Tiefes Lernen in der Medizinischen Bildverarbeitung]. Zeitschrift für Medizinische Physik, 29(2), 86-101. https://dx.doi.org/10.1016/j.zemedi.2018.12.003
Hoppe, E., Wetzl, J., Forman, C., Koerzdoerfer, G., Schneider, M., Speier, P.,... Maier, A. (2019). Free-Breathing, Self-Navigated and Dynamic 3-D Multi-Contrast Cardiac CINE Imaging Using Cartesian Sampling and Compressed Sensing. In Proceedings of the Proceedings of the Joint Annual Meeting ISMRM-ESMRMB (27th Annual Meeting & Exhibition) (pp. 2129). Montreal, CA.
Stromer, D., Vetter, A., Özkan, H.C., Probst, C., & Maier, A. (2019). Enhanced Crack Segmentation (eCS): A Reference Algorithm for Segmenting Cracks in Multicrystalline Silicon Solar Cells. IEEE Journal of Photovoltaics, 9(3), 752-758. https://dx.doi.org/10.1109/JPHOTOV.2019.2895808
Martindale, C., Sprager, S., & Eskofier, B. (2019). Hidden Markov Model-Based Smart Annotation for Benchmark Cyclic Activity Recognition Database Using Wearables. Sensors, 198. https://dx.doi.org/10.3390/s19081820
Jaremenko, C., Ravikumar, N., Affronti, E., Merklein, M., & Maier, A. (2019). Determination of Forming Limits in Sheet Metal Forming Using Deep Learning. Materials, 12(7). https://dx.doi.org/10.3390/ma12071051
Schneider, M., Janas, G., Lugauer, F., Hoppe, E., Nickel, D., Dale, B.M.,... Bashir, M.R. (2019). Accurate fatty acid composition estimation of adipose tissue in the abdomen based on bipolar multi-echo MRI. Magnetic Resonance in Medicine, 81(4), 2330-2346. https://dx.doi.org/10.1002/mrm.27557
Stelzle, F., Oetter, N., Göllner, L., Adler, W., Rohde, M., Maier, A.,... Knipfer, C. (2019). Speech intelligibility in patients with oral cancer: An objective baseline evaluation of pretreatment function and impairment. Head and Neck-Journal For the Sciences and Specialties of the Head and Neck, 41(4), 1063-1069. https://dx.doi.org/10.1002/hed.25527
Mullan, P., Rieß, C., & Freiling, F. (2019). Forensic Source Identification using JPEG Image Headers: The Case of Smartphones. In Digital Investigation. Oslo, NO.
Diaz-Pinto, A., Morales, S., Naranjo, V., Köhler, T., Mossi, J.M., & Navea, A. (2019). CNNs for automatic glaucoma assessment using fundus images: an extensive validation. Biomedical Engineering Online, 18. https://dx.doi.org/10.1186/s12938-019-0649-y

Publications in addition (Download BibTeX)

Hariharan, S.G., Strobel, N., Kaethner, C., Kowarschik, M., Fahrig, R., & Navab, N. (2019). An analytical approach for the simulation of realistic low-dose fluoroscopic images. International Journal of Computer Assisted Radiology and Surgery, 14(4), 601-610. https://dx.doi.org/10.1007/s11548-019-01912-6
Hariharan, S.G., Kaethner, C., Strobel, N., Kowarschik, M., DiNitto, J., Albarqouni, S.,... Navab, N. (2019). Preliminary results of DSA denoising based on a weighted low-rank approach using an advanced neurovascular replication system. International Journal of Computer Assisted Radiology and Surgery. https://dx.doi.org/10.1007/s11548-019-01968-4

Last updated on 2019-24-04 at 10:15