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

  Breininger, Katharina; Hanika, Moritz; Weule, Mareike; Kowarschik, Markus; Pfister, Marcus; Maier, Andreas: Simultaneous reconstruction of multiple stiff wires from a single X-ray projection for endovascular aortic repair In: International Journal of Computer Assisted Radiology and Surgery

  Arbogast, Nikolaus; Kurzendorfer, Tanja; Breininger, Katharina; Mountney, Peter; Toth, Daniel; Narayan, Srinivas A.; Maier, Andreas: Workflow Phase Detection in Fluoroscopic Images Using Convolutional Neural Networks In: Workshop

  Mayr, Martin; Hoffmann, Mathis; Maier, Andreas; Christlein, Vincent: WEAKLY SUPERVISED SEGMENTATION OF CRACKS ON SOLAR CELLS USING NORMALIZED LP NORM In: 2019 IEEE International Conference on Image Processing (ICIP)

  Voigt, Ingmar; Mansi, Tommaso; Georgescu, Bogdan; Houle, Helene; Comaniciu, Dorin; Ene, Codruta; Scutaru, Mihai: Valve regurgitant detection for echocardiography In:

  Voigt, Ingmar; Mansi, Tommaso; Helene, Houle: Measurement Point Determination in Medical Diagnostic Imaging In:

  Camus, Estelle; Mansi, Tommaso; Voigt, Ingmar: Left Atrial Appendage Closure Guidance in Medical Imaging In:

  Scutaru, Mihai; Voigt, Ingmar; Mansi, Tommaso,; Ionasec, Razvan; Houle, Helene; Tatpati, Anand; Comaniciu, Dorin; Georgescu, Bogdan; El-Zehiri, Noha: Landmark detection with spatial and temporal constraints in medical imaging In:

  Henderson, Steve; Mansi, Tommaso; Tatpati, Anand; Voigt, Ingmar; Rao, Bimba: Continuously oriented enhanced ultrasound imaging of a sub-volume In:

  Tek, Huseyin; Georgescu, Bogdan; Mansi, Tommaso; Sauer, Frank; Comaniciu, Dorin; Houle, Helene; Voigt, Ingmar: Cardiac flow detection based on morphological modeling in medical diagnostic ultrasound imaging In:
• Cooperations
  

  • 

    

    

    

    

    

    

    

    

    

    

    

    

    

    

    

    

    

    

    

    

    

    

    

    

    

    

    

    

    

  LME-News
  

  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]

  ---

  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]

  ---

  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]

  ---

  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]

  ---

  BIG-THERA Project in next round of funding

  

  It’s a great pleasure to announce that the BIG-THERA Project’s funding was approved for another year! The Emerging Fields Initiative (EFI) BIG-THERA gathers a multidisciplinary team of highly competitive...[more]

  ---

  Invited Talk by Prof. Dr. Mathias Unberath

  

  Johns Hopkins’ Prof. Dr. Mathias Unberath will give a talk at our lab entitled: "From Acquisition to Augmentation - Towards Task-aware Computer Assistance in the OR“ Abstract: Improved image-guidance has led to the...[more]

  ---

  << First < Previous Page 3 Page 4 Page 5 Page 6 Page 7 Next > Last >>