Publications

Automated objective and marker-free facial grading using photographs of patients with facial palsy..
Oliver Mothes and Luise Modersohn and Gerd Fabian Volk and Carsten Klingner and Otto W. Witte and Peter Schlattmann and Joachim Denzler and Orlando Guntinas-Lichius.
European Archives of Oto-Rhino-Laryngology. 2019.
[bibtex]

One-Shot Learned Priors in Augmented Active Appearance Models for Anatomical Landmark Tracking.
Oliver Mothes and Joachim Denzler.
Computer Vision, Imaging and Computer Graphics -- Theory and Applications. Pages 85-104. 2019.
[bibtex] [abstract]

Machine Learning based Classification of Facial Palsies using Standard Still Photografies.
Orlando Guntinas-Lichius and Oliver Mothes and Gerd F. Volk and Carsten M. Klingner and Otto W. Witte and Peter Schlattmann and Joachim Denzler.
Laryngo-Rhino-Otologie. 98 (S02): pages 360. 2019.
[bibtex] [web]

Long-term home-based Surface Electrostimulation is useful to prevent atrophy in denervated Facial Muscles.
Gerd F. Volk and Martin Thümmel and Oliver Mothes and Dirk Arnold and Jovanna Thielker and Joachim Denzler and Valeria Mastryukova and Winfried Mayr and Orlando Guntinas-Lichius.
Vienna Workshop on Functional Electrical Stimulation (FESWS). 2019.
[bibtex] [abstract]

Self-supervised Data Bootstrapping for Deep Optical Character Recognition of Identity Documents.
Oliver Mothes and Joachim Denzler.
arXiv preprint arXiv:1908.04027. 2019.
[bibtex] [pdf] [abstract]

Active Learning for Regression Tasks with Expected Model Output Changes.
Christoph Käding and Erik Rodner and Alexander Freytag and Oliver Mothes and Björn Barz and Joachim Denzler.
British Machine Vision Conference (BMVC). 2018.
[bibtex] [pdf] [code] [supplementary] [abstract]

Facial motor and non-motor disabilities in patients with central facial paresis: a prospective cohort study.
Gerd F. Volk and Anika Steinerstauch and Annegret Lorenz and Luise Modersohn and Oliver Mothes and Joachim Denzler and Carsten M. Klingner and Farsin Hamzei and Orlando Guntinas-Lichius.
Journal of Neurology. 2018.
[bibtex]

Keeping the Human in the Loop: Towards Automatic Visual Monitoring in Biodiversity Research.
Gerd F. Volk and Rebecca A. Schaede and Jovanna Thielker and Luise Modersohn and Oliver Mothes and Charles C. Nduka and Jodi Maron Barth and Joachim Denzler and Orlando Guntinas-Lichius.
International Conference on Ecological Informatics (ICEI). 2018.
[bibtex] [abstract]

Abstract: More and more methods in the area of biodiversity research grounds upon new opportunities arising from modern sensing devices that in principle make it possible to continuously record sensor data from the environment. However, these opportunities allow easy recording of huge amount of data, while its evaluation is difficult, if not impossible due to the enormous effort of manual inspection by the researchers. At the same time, we observe impressive results in computer vision and machine learning that are based on two major developments: firstly, the increased performance of hardware together with the advent of powerful graphical processing units applied in scientific computing. Secondly, the huge amount of, in part, annotated image data provided by today's generation of Facebook and Twitter users that are available easily over databases (e.g., Flickr) and/or search engines. However, for biodiversity applications appropriate data bases of annotated images are still missing. In this presentation we discuss already available methods from computer vision and machine learning together with upcoming challenges in automatic monitoring in biodiversity research. We argue that the key element towards success of any automatic method is the possibility to keep the human in the loop - either for correcting errors and improving the system's quality over time, for providing annotation data at moderate effort, or for acceptance and validation reasons. Thus, we summarize already existing techniques from active and life-long learning together with the enormous developments in automatic visual recognition during the past years. In addition, to allow detection of the unexpected such an automatic system must be capable to find anomalies or novel events in the data. We discuss a generic framework for automatic monitoring in biodiversity research which is the result of collaboration between computer scientists and ecologists of the past years. The key ingredients of such a framework are initial, generic classifier, for example, powerful deep learning architectures, active learning to reduce costly annotation effort by experts, fine-grained recognition to differentiate between visually very similar species, and efficient incremental update of the classifier's model over time. For most of these challenges, we present initial solutions in sample applications. The results comprise the automatic evaluation of images from camera traps, attribute estimation for species, as well as monitoring in-situ data in environmental science. Overall, we like to demonstrate the potentials and open issues in bringing together computer scientists and ecologist to open new research directions for either area.

Multi-view Anatomical Animal Landmark Localization using Deep Feature Regression.
Oliver Mothes and Joachim Denzler.
International Conference on Pattern Recognition (ICPR) - VAIB workshop. 2018.
[bibtex]

Visual Fish Tracking: Combining a Two-Stage Graph Approach with CNN-Features.
Jonas Jäger and Oliver Mothes and Viviane Wolff and Klaus Fricke-Neuderth and Joachim Denzler.
MTS/IEEE OCEANS Conference Aberdeen. Pages 1-6. 2017.
[bibtex] [pdf]

Anatomical Landmark Tracking by One-shot Learned Priors for Augmented Active Appearance Models.
Oliver Mothes and Joachim Denzler.
International Conference on Computer Vision Theory and Applications (VISAPP). Pages 246-254. 2017.
[bibtex] [pdf] [web]

Visualization of Barium Titanate Phase Transition using Quantization of Dipole Moment Vectors.
Henry Lehmann and Patrick Heyne and Oliver Mothes and Heiko Müller and Tim Smyth and Erik Berger and Katja Fiedler and David Vogt and Bernhard Jung.
Electronic Proceedings IEEE VisWeek. 2012. Third Place Award IEEE SciVis Contest 2012
[bibtex] [pdf] [abstract]