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
  

  New research member

  

  Jonathan Lommen joined the Pattern Recognition Lab on February 1st and is part of the Magnetic Resonance Imaging group. He is a new member in the „P3 Stroke“ project in collaboration with the Department of...[more]

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  Virtuelle Hochschule Bayern Is Funding New Online Course for Medical Engineering

  

  We are happy to announce that the Virtuelle Hochschule Bayern (VHB) supports the project "Medical Image Processing for Interventional Applications".In succession of the completed course project "MIPDA", which...[more]

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  IEEE SPS Award

  

  On behalf of the IEEE Signal Processing Society (SPS) our paper got awarded with the honorable IEEE SPS Award: Christlein, Vincent; Riess, Christian; Jordan, Johannes; Riess, Corinna; Angelopoulou, Elli: "An Evaluation of...[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 he/she applied for the NVIDIA GPU Grant Program:https://developer.nvidia.com/academic_gpu_seeding Shuqing Chen received a grant for her research on quantitative...[more]

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  ICDAR 2017: Competition win

  

  The joint team (Mike Kestemont from the University of Antwerp, Vincent Christlein from the LME) participated in a competition of the International Conference on Document Analysis and Recognition (ICDAR). In the "ICDAR2017...[more]

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  New Project funded by the Leibnitz Association

  

  The project "Critical Catalog of the Luther-Portraits (1519-1530)" got approved by the Leibnitz Association as one of four funded projects of the 2018 Leibnitz Competition. The project is an interdisciplinary project in...[more]

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