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

  Image Analysis for Microscopical Layer Characterization and Morphological Feature Parametrization (Master Arbeit) Betreuer: Maier, Andreas; Steidl, Stefan; Spies, Ines

  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

  Stutzmann, Dominique; Tensmeyer, Christopher; Christlein, Vincent: Writer identification and script classification: two tasks for a common understanding of cultural heritage In: OpenX for Interdisciplinary Computational Manuscript Research

  Preuhs, Alexander; Berger, Martin; Bauer, Sebastian; Redel, Thomas; Unberath, Mathias; Achenbach, Stephan; Maier, Andreas: Viewpoint Planning for Quantitative Coronary Angiography (IJCARS) In: International Journal of Computer Assisted Radiology and Surgery

  Aubreville, Marc; Stöve, Maike; Oetter, Nicolai; de Jesus Goncalves, Miguel; Knipfer, Christian; Neumann, Helmut; Bohr, Christopher; Stelzle, Florian; Maier, Andreas: Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images In: International Journal of Computer Assisted Radiology and Surgery

  Mentl, Katrin; Saffoury, Rimon; Maier, Andreas: Abstract: Automatic Malignancy Estimation for Pulmonary Nodules from CT Images In: Bildverarbeitung für die Medizin

  Christlein, Vincent: Automatic Detection of Illuminated Charters In: Illuminierte Urkunden. Beiträge aus Diplomatik, Kunstgeschichte und Digital Humanities/Illuminated Charters. Essays from Diplomatic, Art History and Digital Humanities

  Maass, Nicole; Maier, Andreas; Würfl, Tobias: Reducing Image Artifacts In:

  Mayer, Markus: Automated Glaucoma Detection with Optical Coherence Tomography In:

  Tomás Arias-Vergara; Juan Camilo Vásquez-Correa; Orozco-Arroyave, Juan Rafael; Nöth, Elmar: Speaker models for monitoring Parkinson's disease progression considering different communication channels and acoustic conditions In: Speech Communication

  Laureano Moro-Velázquez; Jorge Andrés Gómez-García; Juan Ignacio Godino-Llorente; Jesús Villalba; Orozco-Arroyave, Juan Rafael; Najim Dehak: Analysis of speaker recognition methodologies and the influence of kinetic changes to automatically detect Parkinson's disease In: Applied Soft Computing
• Cooperations
  

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  LME-News
  

  LME Successful at Rural Health Hackathon

  

  Tobias Würfl, PhD student at the Pattern Recognition Lab and his team won an award for the best business idea at the hacking rural health hackathon (link). The hackathon was focussed on specific healthcare challenges arising in...[more]

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  New PhD Student

  

  Aline Sindel joined the Pattern Recognition Lab on April 1st and is part of the Computer Vision Group. She received her Bachelor and Master of Science in Medical Engineering from the Friedrich-Alexander-Universität...[more]

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  DAS 2018: Best Student Paper Award

  

  Daniel Stromer, PhD student at the Pattern Recognition Lab, has received the Best Student Paper Award at the 13th IAPR International Workshop on Document Analysis Systems (DAS 2018) for his publication entitled ...[more]

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  Students Receive Newcomer Awards

  

  Elisabeth Hoppe (right), PhD candidate at the Pattern Recognition Lab and Maike Stöve (left), a former student of the Pattern Recognition Lab, made the second and third places at the “Connecting Healthcare IT” Newcomer Award for...[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 Julia Schottenhamml received a grant for her research on the...[more]

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  New PhD Student

  

  Christian Bergler joined the Pattern Recognition Lab on April 1st and is part of the group Speech Processing and Understanding (SAGI). He received his Bachelor of Engineering in Industrial Engineering (focus on Mechanical...[more]

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