@article{slijepcevic_explaining_2022, title = {Explaining {Machine} {Learning} {Models} for {Clinical} {Gait} {Analysis}}, volume = {3}, copyright = {CC-BY-NC-SA}, issn = {2691-1957}, url = {https://doi.org/10.1145/3474121}, doi = {10.1145/3474121}, number = {2}, journal = {ACM Transactions on Computing for Healthcare}, author = {Slijepcevic, Djordje and Horst, Fabian and Lapuschkin, Sebastian and Horsak, Brian and Raberger, Anna-Maria and Kranzl, Andreas and Samek, Wojciech and Breitender, Christian and Schöllhorn, Wolfgang and Zeppelzauer, Matthias}, year = {2022}, note = {Projekt: I3D Projekt: ReMoCapLab Projekt: DHLab}, keywords = {2020, Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, DHLab, Department Gesundheit, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, SP CDHSI Motor Rehabilitation, Studiengang Physiotherapie, Wiss. Beitrag, best, best-mzeppelzauer, peer-reviewed}, pages = {14:1--14:27}, } @inproceedings{baumhauer_bounded_2022, address = {New Orleans, USA}, title = {Bounded logit attention: {Learning} to explain image classifiers}, url = {https://arxiv.org/pdf/2105.14824}, author = {Baumhauer, Thomas and Slijepcevic, Djordje and Zeppelzauer, Matthias}, year = {2022}, keywords = {Center for Artificial Intelligence, Data Science, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Visual Computing, Vortrag, Wiss. Beitrag, best-mzeppelzauer, ⛔ No DOI found}, } @inproceedings{kirchknopf_explaining_2021, title = {Explaining {YOLO}: {Leveraging} {Grad}-{CAM} to {Explain} {Object} {Detections}}, isbn = {978-3-85125-869-1}, url = {https://openlib.tugraz.at/download.php?id=621f34738ca16&location=browse}, doi = {10.3217/978-3-85125-869-1-13}, booktitle = {Proceedings of the {Workshop} of the {Austrian} {Association} for {Pattern} {Recognition}}, publisher = {TU Graz}, author = {Kirchknopf, Armin and Slijepcevic, Djordje and Wunderlich, Ilkay and Breiter, Michael and Traxler, Johannes and Zeppelzauer, Matthias}, month = may, year = {2021}, keywords = {Center for Artificial Intelligence, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Wiss. Beitrag, best-estumpe, peer-reviewed, ⛔ No DOI found}, pages = {3}, } @article{slijepcevic_input_2020, title = {Input {Representations} and {Classification} {Strategies} for {Automated} {Human} {Gait} {Analysis}}, volume = {76}, issn = {0966-6362}, doi = {10/ghz24x}, journal = {Gait \& Posture}, author = {Slijepcevic, Djordje and Zeppelzauer, Matthias and Schwab, Caterine and Raberger, Anna-Maria and Breitender, Christian and Horsak, Brian}, year = {2020}, note = {Projekt: IntelliGait Projekt: I3D Projekt: ReMoCap-Lab Projekt: DHLab}, keywords = {2020, Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Department Gesundheit, Eintrag überprüfen, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Green OA, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, Open Access, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Wiss. Beitrag, best, best-bhorsak, peer-reviewed}, pages = {198--203}, } @inproceedings{slijepcevic_usefulness_2019, address = {Vienna, Austria}, title = {On the usefulness of statistical parameter mapping for feature selection in automated gait classification}, booktitle = {Book of {Abstracts} of the 25th {Conference} of the {European} {Society} of {Biomechanics} ({ESB})}, author = {Slijepcevic, Djordje and Raberger, Anna-Maria and Zeppelzauer, Matthias and Dumphart, Bernhard and Breiteneder, Christian and Horsak, Brian}, year = {2019}, note = {Projekt: IntelliGait Projekt: DHLab}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Digital Health, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Vortrag, Wiss. Beitrag, best, best-bhorsak, peer-reviewed, ⛔ No DOI found}, pages = {1}, } @article{wagner_kavagait_2018, title = {{KAVAGait}: {Knowledge}-{Assisted} {Visual} {Analytics} for {Clinical} {Gait} {Analysis}}, volume = {25}, url = {https://doi.org/10.1109/TVCG.2017.2785271}, doi = {10/ghppzn}, abstract = {In 2014, more than 10 million people in the US were affected by an ambulatory disability. Thus, gait rehabilitation is a crucial part of health care systems. The quantification of human locomotion enables clinicians to describe and analyze a patient’s gait performance in detail and allows them to base clinical decisions on objective data. These assessments generate a vast amount of complex data which need to be interpreted in a short time period. We conducted a design study in cooperation with gait analysis experts to develop a novel Knowledge-Assisted Visual Analytics solution for clinical Gait analysis (KAVAGait). KAVAGait allows the clinician to store and inspect complex data derived during clinical gait analysis. The system incorporates innovative and interactive visual interface concepts, which were developed based on the needs of clinicians. Additionally, an explicit knowledge store (EKS) allows externalization and storage of implicit knowledge from clinicians. It makes this information available for others, supporting the process of data inspection and clinical decision making. We validated our system by conducting expert reviews, a user study, and a case study. Results suggest that KAVAGait is able to support a clinician during clinical practice by visualizing complex gait data and providing knowledge of other clinicians.}, number = {3}, journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)}, author = {Wagner, Markus and Slijepcevic, Djordje and Horsak, Brian and Rind, Alexander and Zeppelzauer, Matthias and Aigner, Wolfgang}, year = {2018}, note = {Projekt: KAVA-Time Projekt: IntelliGait Projekt: CARMA Projekt: DHLab}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, DHLab, Design Study, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Digital Technologies, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Healthcare, Human Gait Analysis, Human-Computer Interaction, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Visual analytics, Wiss. Beitrag, best, best-bhorsak, best-lbaigner, best-lbwagnerm, best-mzeppelzauer, information visualization, knowledge generation, peer-reviewed}, pages = {1528--1542}, } @article{slijepcevic_automatic_2018, title = {Automatic {Classification} of {Functional} {Gait} {Disorders}}, volume = {5}, issn = {2168-2194}, url = {https://arxiv.org/abs/1712.06405}, doi = {10/ghz24w}, number = {22}, urldate = {2017-12-21}, journal = {IEEE Journal of Biomedical and Health Informatics}, author = {Slijepcevic, Djordje and Zeppelzauer, Matthias and Raberger, Anna-Maria and Schwab, Caterine and Schuller, Michael and Baca, Arnold and Breiteneder, Christian and Horsak, Brian}, year = {2018}, note = {Projekt: IntelliGait Projekt: CARMA Projekt: DHLab}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, DHLab, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Wiss. Beitrag, best, best-bhorsak, best-mzeppelzauer, peer-reviewed}, pages = {1653 -- 1661}, } @inproceedings{horsak_explainable_2020, address = {München, Deutschland}, title = {Explainable {Artificial} {Intelligence} ({XAI}) und ihre {Anwendung} auf {Klassifikationsprobleme} in der {Ganganalyse}}, booktitle = {Abstractband des 3. {GAMMA} {Kongress}}, author = {Horsak, Brian and Dumphart, Bernhard and Slijepcevic, Djordje and Zeppelzauer, Matthias}, year = {2020}, note = {Projekt: ReMoCap-Lab Projekt: DHLab Projekt: I3D}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Department Gesundheit, Eintrag überprüfen, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Green OA, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Publikationstyp Präsentation, Publikationstyp Schriftpublikation, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Vortrag, Wiss. Beitrag, best, best-bhorsak, peer-reviewed, ⛔ No DOI found}, } @article{horsak_gaitrec_2020, title = {{GaitRec}, a large-scale ground reaction force dataset of healthy and impaired gait}, volume = {7:143}, copyright = {CC BY}, url = {https://www.nature.com/articles/s41597-020-0481-z}, doi = {10/gh372d}, number = {1}, journal = {Scientific Data}, author = {Horsak, Brian and Slijepcevic, Djordje and Raberger, Anna-Maria and Schwab, Caterine and Worisch, Marianne and Zeppelzauer, Matthias}, year = {2020}, note = {Projekt: I3D Projekt: IntelliGait Projekt: DHLab}, keywords = {2019, Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, DHLab, Eintrag überprüfen, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Green OA, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, Open Access, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Wiss. Beitrag, best, best-bhorsak, best-mzeppelzauer, peer-reviewed, submitted}, pages = {1--8}, } @inproceedings{schwab_intelligait_2018, address = {Hamburg, Deutschland}, title = {{IntelliGait}: {Automatische} {Gangmusteranalyse} für die robuste {Erkennung} von {Gangstörungen}}, booktitle = {Tagungsband des 2ten {GAMMA} {Kongress} ({Gesellschaft} für die {Analyse} {Menschlicher} {Motorik} in ihrer klinischen {Anwendung})}, author = {Schwab, Caterine and Slijepcevic, Djordje and Zeppelzauer, Matthias and Raberger, Anna-Maria and Dumphart, Bernhard and Baca, Arnold and Breitender, Christian and Horsak, Brian}, year = {2018}, note = {Projekt: IntelliGait Projekt: CARMA Projekt: DHLab}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Creative Industries, DHLab, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, Pattern recognition, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Wiss. Beitrag, best, best-bhorsak, peer-reviewed, ⛔ No DOI found}, } @inproceedings{slijepcevic_ground_2017, address = {Trondheim, Norway}, title = {Ground reaction force measurements for gait classification tasks: {Effects} of different {PCA}-based representations}, volume = {57}, url = {http://www.gaitposture.com/article/S0966-6362(17)30712-9/pdf}, doi = {10.1016/j.gaitpost.2017}, booktitle = {Gait \& {Posture} {Supplement}}, author = {Slijepcevic, Djordje and Horsak, Brian and Schwab, Caterine and Raberger, Anna-Maria and Schüller, Michael and Baca, Arnold and Breitender, Christian and Zeppelzauer, Matthias}, year = {2017}, note = {Projekt: IntelliGait Projekt: DHLab}, keywords = {2017, Biofeedback, Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Creative Industries, DHLab, Department Technologie, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, Pattern recognition, Publikationstyp Präsentation, Publikationstyp Schriftpublikation, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Wiss. Beitrag, best, best-bhorsak, peer-reviewed, project\_carma, project\_intelligait, ⚠️ Invalid DOI}, pages = {4--5}, } @inproceedings{slijepcevic_towards_2018, address = {Prague, Czech Republic}, title = {Towards an optimal combination of input signals and derived representations for gait classification based on ground reaction force measurements.}, volume = {65}, doi = {10/gh38wn}, booktitle = {Gait \& {Posture} {Supplement}}, author = {Slijepcevic, Djordje and Zeppelzauer, Matthias and Schwab, Caterine and Raberger, Anna-Maria and Dumphart, B and Baca, Arnold and Breiteneder, Christian and Horsak, Brian}, year = {2018}, note = {Projekt: IntelliGait Projekt: CARMA Projekt: DHLab}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health and Social Innovation, Classification, DHLab, FH SP Data Analytics \& Visual Computing, Feature Representations, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Gait Recognition, Human Gait Analysis, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, PCA, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, SVM, Wiss. Beitrag, best, best-bhorsak, pattern recognition, peer-reviewed}, } @inproceedings{slijepcevic_usefullness_2019, address = {Vienna, Austria}, title = {On the usefullness of statistical parameter mapping for feature selection in automated gait classification}, booktitle = {Book of {Abstracts} of the 25th {Conference} of the {European} {Society} of {Biomechanics} ({ESB})}, author = {Slijepcevic, Djordje and Raberger, Anna-Maria and Zeppelzauer, Matthias and Dumphart, Bernhard and Breiteneder, Christian and Horsak, Brian}, year = {2019}, note = {Projekt: IntelliGait Projekt: DHLab}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health and Social Innovation, DHLab, Digital Health, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Vortrag, Wiss. Beitrag, best, peer-reviewed, ⛔ No DOI found}, pages = {1}, } @inproceedings{iber_mind_2021, title = {Mind the {Steps}: {Towards} {Auditory} {Feedback} in {Tele}-{Rehabilitation} {Based} on {Automated} {Gait} {Classification}}, doi = {10/gnt2tc}, abstract = {We describe a proof-of-concept for the implementation of a mobile auditory biofeedback system based on automated classification of functional gait disorders. The classification is embedded in a sensor-instrumented insole and is based on ground reaction forces (GRFs). GRF data have been successfully used for the classification of gait patterns into clinically relevant classes and are frequently used in clinical practice to quantitatively describe human motion. A feed-forward neural network that was implemented on the firmware of the insole is used to estimate the GRFs using pressure and accelerator data. Compared to GRF measurements obtained from force plates, the estimated GRFs performed highly accurately. To distinguish between physiological gait and gait disorders, we trained and evaluated a support vector machine with labeled data from a publicly accessible database. The automated gait classification was sonified for auditory feedback. The high potential of the implemented auditory feedback for preventive and supportive applications in physical therapy, such as supervised therapy settings and tele-rehabilitation, was highlighted by a semi- structured interview with two experts.}, booktitle = {In {Proceedings} of the 16th {International} {Audio} {Mostly} {Conference} ({AM}’21)}, publisher = {ACM}, author = {Iber, Michael and Dumphart, Bernhard and Oliveira, Victor A. de. J. and Ferstl, Stefan and Reis, Joschua and Slijepcevic, Djordje and Heller, Mario and Raberger, Anna-Maria and Horsak, Brian}, year = {2021}, note = {Projekt: Sonigait II}, keywords = {Artificial Intelligence, Biofeedback, Biomechanics, CDHI, Digital Health, Forschungsgruppe Media Computing, Gait Analysis, Human-computer interaction, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Motor rehabilitation, Vortrag, Wiss. Beitrag, best, best-lbiber, peer-reviewed}, } @misc{slijepcevic_explanation_2020, address = {Wien}, type = {Invited {Talk}}, title = {Explanation of {Automatic} {Predictions} in {Human} {Gait} {Analysis}}, language = {English}, author = {Slijepcevic, Djordje}, month = feb, year = {2020}, note = {Projekt: I3D}, keywords = {Computer Vision, Department Medien und Digitale Technologien, Department Technologie, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Invited Talk, Machine Learning, Media Computing Group, Präsentation, Publikationstyp Vortrag, Wiss. Beitrag}, } @inproceedings{kirchknopf_multimodal_2021, address = {Lille, France}, title = {Multimodal {Detection} of {Information} {Disorder} from {Social} {Media}}, url = {https://ieeexplore.ieee.org/document/9461898}, doi = {10/gmxnm5}, abstract = {Social media is accompanied by an increasing proportion of content that provides fake information or misleading content, known as information disorder. In this paper, we study the problem of multimodal fake news detection on a largescale multimodal dataset. We propose a multimodal network architecture that enables different levels and types of information fusion. In addition to the textual and visual content of a posting, we further leverage secondary information, i.e. user comments and metadata. We fuse information at multiple levels to account for the specific intrinsic structure of the modalities. Our results show that multimodal analysis is highly effective for the task and all modalities contribute positively when fused properly.}, language = {en}, urldate = {2021-07-12}, booktitle = {International {Conference} on {Content}-{Based} {Multimedia} {Indexing} ({CBMI})}, publisher = {IEEE}, author = {Kirchknopf, Armin and Slijepcevic, Djordje and Zeppelzauer, Matthias}, year = {2021}, note = {arXiv: 2105.15165}, keywords = {Artificial Intelligence, Center for Artificial Intelligence, Digital Media, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Poster, Wiss. Beitrag, best-akirchknopf, best-mzeppelzauer, peer-reviewed}, pages = {4}, } @inproceedings{vyssoki_kompetenzorientierte_2019, address = {St. Pölten}, title = {Kompetenzorientierte {Prüfungsdesigns} - {DataBootCamp} und {Parallel} {Escape} {Rooms}}, isbn = {978-3-99023-550-8}, url = {http://skill.fhstp.ac.at/wp-content/uploads/2019/11/Tagungsband_2019.pdf}, booktitle = {Gelernt wird, was geprüft wird“, oder…?! {Assessment} in der {Hochschullehre} neu denken: {Good} {Practices} – {Herausforderungen} – {Visionen}}, publisher = {ALGE Verlag}, author = {Vyssoki, Sandra and Stoiber, Christina and Slijepcevic, Djordje and Wagner-Havlicek, Carina and Wagner, Miriam and Wagner, Markus}, month = oct, year = {2019}, keywords = {Forschungsgruppe Digital Technologies, Institut für Creative Media Technologies, Wiss. Beitrag, peer-reviewed}, } @inproceedings{boeck_ait_fhstp_2021, address = {Duesseldorf, Germany}, title = {{AIT}\_FHSTP at {GermEval} 2021: {Automatic} {Fact} {Claiming} {Detection} with {Multilingual} {Transformer} {Models}}, url = {https://aclanthology.org/2021.germeval-1.11.pdf}, booktitle = {Proceedings of the {GermEval} 2021 {Shared} {Task} on the {Identification} of {Toxic}, {Engaging}, and {Fact}-{Claiming} {Comments}}, publisher = {Association for Computational Linguistics}, author = {Boeck, Jaqueline and Liakhovets, Daria and Schütz, Mina and Kirchknopf, Armin and Slijepcevic, Djordje and Zeppelzauer, Matthias and Schindler, Alexander}, year = {2021}, keywords = {Center for Artificial Intelligence, Data Science, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Open Access, Visual Computing, Visual analytics, Vortrag, Wiss. Beitrag, peer-reviewed, ⛔ No DOI found}, pages = {76--82}, } @article{horst_gutenberg_2021, title = {Gutenberg {Gait} {Database}, a ground reaction force database of level overground walking in healthy individuals}, volume = {8}, copyright = {Open Access}, issn = {2052-4463}, url = {https://www.nature.com/articles/s41597-021-01014-6}, doi = {https://doi.org/10.1038/s41597-021-01014-6}, abstract = {The Gutenberg Gait Database comprises data of 350 healthy individuals recorded in our laboratory over the past seven years. The database contains ground reaction force (GRF) and center of pressure (COP) data of two consecutive steps measured - by two force plates embedded in the ground - during level overground walking at self-selected walking speed. The database includes participants of varying ages, from 11 to 64 years. For each participant, up to eight gait analysis sessions were recorded, with each session comprising at least eight gait trials. The database provides unprocessed (raw) and processed (ready-to-use) data, including three-dimensional GRF and two-dimensional COP signals during the stance phase. These data records offer new possibilities for future studies on human gait, e.g., the application as a reference set for the analysis of pathological gait patterns, or for automatic classification using machine learning. In the future, the database will be expanded continuously to obtain an even larger and well-balanced database with respect to age, sex, and other gait-specific factors.}, language = {en}, number = {1}, urldate = {2021-09-02}, journal = {Scientific Data}, author = {Horst, Fabian and Slijepcevic, Djordje and Simak, Marvin and Schöllhorn, Wolfgang I.}, month = sep, year = {2021}, note = {Projekt: I3D}, keywords = {Center for Artificial Intelligence, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Wiss. Beitrag, dataset, peer-reviewed}, pages = {232}, } @inproceedings{strebl_how_2020, address = {Graz (Austria)}, title = {How {High} is the {Tide}? {Estimation} of {Flood} {Level} from {Social} {Media}}, doi = {10/gnt2wh}, booktitle = {Proceedings of {Joint} {Austrian} {Computer} {Vision} and {Robotics} {Workshop} 2020}, publisher = {TU Graz}, author = {Strebl, Julia and Slijepcevic, Djordje and Kirchknopf, Armin and Sakeena, Muntaha and Seidl, Markus and Zeppelzauer, Matthias}, year = {2020}, keywords = {Center for Artificial Intelligence, Computer Vision, Data Science, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Open Access, Visual Computing, Wiss. Beitrag, peer-reviewed}, pages = {2}, } @inproceedings{strebl_flood_2019, address = {Nice, France}, title = {Flood {Level} {Estimation} from {Social} {Media} {Images}}, volume = {2670}, booktitle = {{CEUR} {Proceedings} of the {MediaEval} 2019 {Workshop}}, author = {Strebl, Julia and Slijepcevic, Djordje and Kirchknopf, Armin and Sakeena, Muntaha and Seidl, Markus and Zeppelzauer, Matthias}, year = {2019}, keywords = {Center for Artificial Intelligence, Computer Vision, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Vortrag, Wiss. Beitrag, ⛔ No DOI found}, pages = {2}, } @inproceedings{kirchknopf_detection_2018, address = {Sophia Antipolis, France}, title = {Detection of {Road} {Passability} from {Social} {Media} and {Satellite} {Images}}, abstract = {This paper presents the contribution of Team MC-FHSTP to the multimedia satellite task at the MediaEval 2018 benchmark. We present two methods, one for the estimation of the passability of roads from social media images due to flooding and one method that estimates passability from satellite images. We present the results obtained in the benchmark for both methods.}, booktitle = {{CEUR} {Proceedings} of the {MediaEval} 2018 {Workshop}}, author = {Kirchknopf, Armin and Slijepcevic, Djordje and Zeppelzauer, Matthias and Seidl, Markus}, year = {2018}, keywords = {Center for Artificial Intelligence, Computer Vision, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Vortrag, Wiss. Beitrag, ⛔ No DOI found}, pages = {2}, }