Lehrstuhl für Informatik 5 (Mustererkennung)


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).

Martensstraße 3
91058 Erlangen

Publications (Download BibTeX)
1 of 62

Journal article
Kautz T, Eskofier B, Pasluosta CF (2017)
Generic performance measure for multiclass-classifiers
Pattern Recognition

Conference contribution
Aubreville M, Krappmann M, Klopfleisch R, et al. - Ed.: Stefan Bruckner, Anja Hennemuth, Bernhard Kainz, et al. (2017)
A Guided Spatial Transformer Network
for Histology Cell Differentiation
Eurographics Workshop on Visual Computing for Biology and Medicine

Journal article
Hannink J, Kautz T, Pasluosta CF, et al. (2017)
Mobile Stride Length Estimation with Deep Convolutional Neural Networks.
IEEE Journal of Biomedical and Health Informatics

Journal article
Groh B, Fleckenstein M, Kautz T, et al. (2017)
Classification and visualization of skateboard tricks using wearable sensors
Pervasive and Mobile Computing

Conference contribution
Evert S, Wankerl S, Nöth E (2017)
Reliable measures of syntactic and lexical complexity: The case of Iris Murdoch
Proceedings of the Corpus Linguistics 2017 Conference

Article in Edited Volumes
Leutheuser H, Lang N, Gradl-Trautvetter S, et al. - Ed.: Stefan Schneegass, Oliver Amft (2017)
Textile Integrated Wearable Technologies for Sports and Medical Applications
Smart Textiles - Human–Computer Interaction Series

Journal article
Hannink J, Kautz T, Pasluosta CF, et al. (2017)
Sensor-based Gait Parameter Extraction with Deep Convolutional Neural Networks.
IEEE Journal of Biomedical and Health Informatics

Journal article
Kluge F, Krinner S, Lochmann M, et al. (2017)
Speed dependent effects of laterally wedged insoles on gait biomechanics in healthy subjects
Gait & Posture

Conference contribution
Gelse K, Knorz S, Kluge F, et al. (2017)
„Natural Running“: Biomechanischer Vergleich von Vorfuß- oder Rückfußlauf
Sports Orthopaedics and Traumatology

Journal article
Leutheuser H, Heyde C, Röcker K, et al. (2017)
Reference-Free Adjustment of Respiratory Inductance Plethysmography for Measurements during Physical Exercise
IEEE Transactions on Biomedical Engineering

Journal article
Kautz T, Groh B, Hannink J, et al. (2017)
Activity recognition in beach volleyball using a Deep Convolutional Neural Network
Data Mining and Knowledge Discovery

Journal article
Knorz S, Kluge F, Gelse K, et al. (2017)
Three-Dimensional Biomechanical Analysis of Rearfoot and Forefoot Running
Orthopaedic Journal of Sports Medicine

Journal article
Christian J, Kluge F, Eskofier B, et al. (2017)
Comparison of different marker sets for marker trajectory and principal component analysis based classification of simulated gait impairments
Journal of Biomedical Engineering and Informatics

Journal article
Magaraggia J, Wei W, Weiten M, et al. (2017)
Design and evaluation of a portable intra-operative unified-planning-and-guidance framework applied to distal radius fracture surgery
International journal of computer assisted radiology and surgery

Journal article
Ring M, Lohmueller C, Rauh M, et al. (2017)
Salivary Markers for Quantitative Dehydration Estimation During Physical Exercise
IEEE Journal of Biomedical and Health Informatics

Conference contribution
Morgner P, Müller C, Ring M, et al. (2017)
Privacy Implications of Room Climate Data
Proceedings of the 22nd European Symposium on Research in Computer Security

Journal article
Aubreville M, Knipfer C, Oetter N, et al. (2017)
Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning
Scientific Reports

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Last updated on 2016-05-04 at 09:57
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