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
  

  LME Successful at MIT Hackathon

  

  Daniel Stromer and Lennart Husvogt, both PhD students at the Pattern Recognition Lab, were part of one the winning teams of the MIT Media Lab's CityRobotics Hackathon. The hackathon focused on the intersection of transportation,...[more]

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  PRL on title of local newspaper

  

  On Wednesday 29th of August our lab made it onto the title of the NZ with the European Time Machine Project. An Interview with more details is found on Page 20. Have fun reading the news![more]

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  "Medical Imaging Systems - An Introductory Guide" published

  

  It is a great pleasure to announce that our new text book "Medical Imaging Systems - An Introductory Guide“ has been published as open access version under this...[more]

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  Featured Story on Scale-Space Anisotropic Total Variation

  

  Our recently published paper "Scale-space Anisotropic Total Variation for Limited Angle Tomography" has been selected as one of the "featured stories" by the journal IEEE Transactions on Radiation and Plasma...[more]

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  Another NVIDIA GPU Grant Approved for PRL Member

  The NVIDIA Corporation donated a GPU to a member of the lab after she applied for the NVIDIA GPU Grant Program: https://developer.nvidia.com/academic_gpu_seeding Aline Sindel received a grant for her research on deep similarity...[more]

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  New Employee

  

  Alexandra Hauske joined the Pattern Recognition Lab on June 15th.   After training as a foreign language correspondent (in Spanish and English) at the Institut für Fremdsprachen und Auslandskunde at the...[more]

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