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). 

• Research Areas
  

  • Pattern Recognition & Machine Learning

    

    Pattern Recognition deals with the automatic classification and analysis of sensor input data. In this area we have work groups in Speech Recognition and Understanding, Computer Vision, Multiple Criteria Optimization, Image Analysis, Image Segmentation, and Image Fusion.

    Medical Image Processing

    

    The research area medical image processing investigates formation and analysis of images in medicine. Currently the groups Analytic Reconstruction and Consistency, Computed Tomography: Algebraic Reconstruction and Motion, Magnetic Resonance Imaging, Phase-Contrast Imaging, and Ophthalmic Imaging are working on such topics.

    Big Data Applications

    

    With the sheer amount of "big data", new opportunities are available also for pattern recognition and machine learning. Examples are deep learning and precision learning. In this area, we have the groups of Learning Approaches for Medical Big Data Analysis, Population Modelling, and Enterprise Computing.

  Available Theses
  

  Extension of an audio-recordings database with features for similarity search (Master Arbeit) Betreuer: Maier, Andreas; Meyer-Wegener, Klaus

  Recent Publications
  

  Choi, Jang-hwan; McWalter, Emily; Datta, Sanjit; Müller, Kerstin; Maier, Andreas; Gold, Garry; Levenston, Marc; Fahrig, Rebecca: In Vivo 3D Measurement of Time-dependent Human Knee Joint Compression and Cartilage Strain During Static Weight-Bearing In: ORS 2016 Annual Meeting

  Geimer, Tobias; Ploner, Stefan; Keall, Paul; Bert, Christoph; Maier, Andreas: Respiratory Deformation Estimation in X-ray Guided IMRT Using a Bilinear Model In: (BVM)

  Preuhs, Alexander; Manhart, Michael; Roser, Philipp; Stimpel, Bernhard; Syben, Christopher; Psychogios, Marios; Kowarschik, Markus; Maier, Andreas: Image Quality Assessment for Rigid Motion Compensation In: Medical Imaging Meets Neurips

  Schottenhamml, Julia; Ploner, Stefan; Moult, Eric; Duker, Jay; Waheed, Nadia; Fujimoto, James; Maier, Andreas: Deep learning based hybrid OCT-OCTA segmentation of Bruch’s membrane in pathology In: ARVO Annual Meeting 2019

  Wasza, Jakob: Real-time Respiratory Motion Analysis Using GPU-accelerated Range Imaging In:

  Maier, Andreas; Syben, Christopher; Stimpel, Bernhard; Würfl, Tobias; Hoffmann, Mathis; Schebesch, Frank; Fu, Weilin; Mill, Leonid; Kling, Lasse; Christiansen, Silke: Learning with known operators reduces maximum error bounds In: Nature Machine Intelligence

  Fu, Weilin; Breininger, Katharina; Schaffert, Roman; Ravikumar, Nishant; Maier, Andreas: A Divide-and-Conquer Approach towards Understanding Deep Networks In: MICCAI

  Schaffert, Roman; Wang, Jian; Fischer, Peter; Maier, Andreas; Borsdorf, Anja: Robust Multi-View 2-D/3-D Registration Using Point-to-Plane Correspondence Model In: IEEE Transactions on Medical Imaging

  Amrehn, Mario; Steidl, Stefan; Kortekaas, Reinier; Strumia, Maddalena; Weingarten, Markus; Kowarschik, Markus; Maier, Andreas: A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems In: International Journal of Biomedical Imaging

  Hoppe, Elisabeth; Thamm, Florian; Körzdörfer, Gregor; Syben, Christopher; Schirrmacher, Franziska; Nittka, Mathias; Pfeuffer, Josef; Meyer, Heiko; Maier, Andreas: Magnetic Resonance Fingerprinting Reconstruction Using Recurrent Neural Networks In: German and Medical Data Sciences: Shping Change - Creative Solutions for Innovative Medicine
• Cooperations
  

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  LME-News
  

  Invited Talk by Natasha Elizabeth Morton

  On next Monday, Natasha Elizabeth Morton from ACRF Image X Institute from Sydney university will give a talk in our lab.   Titel: "Reducing imaging artefacts at the root of the cause: Respiratory Adaptive...[more]

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  New PhD student

  Prathmesh Madhu started working with the Pattern Recognition Lab as PhD researcher under Prof. Andreas Maier on the topic -"Scene understanding using deep learning" from December, 2018. He has a working experience of 2...[more]

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  Emerging Fields Initiative funds "Tapping the potential of Earth Observation (TAPE)“

  

  Earth Observation is producing massive amounts of data that are currently often processed with simple methods such as heuristically found rule-based systems. Our vision are end-to-end automated processing chains...[more]

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  Emerging Fields Project in High-Field MR Bone Imaging granted

  

  The Pattern Recognition Lab was successful in a second EFI Project. A project consortium of medical experts, medical physicists, and computer scientists (Prof. Krönke, Prof. Nagel, Prof. Bäuerle, PD Dr. Römer, Dr. Pachowsky, Dr....[more]

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  New EFI Project awarded in Digital Humanities

  

  It’s a great please to announce that a joint group of humanities and computer science researchers (Prof. Bell, Prof. Verstegen, Prof. Reinhardt, and Prof. Maier) have been successful to acquire a two-year research project within...[more]

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  12.3 Mio EUR for researchers at FAU and HZB

  

  In an ageing European society, the number of people suffering from osteoporosis has increased dramatically. This disease considerably impairs patients' life quality, and results in high societal costs. However, current...[more]

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