@article{SchornVerhoevenRidderetal.2021, author = {Schorn, Michelle A. and Verhoeven, Stefan and Ridder, Lars and Huber, Florian and Acharya, Deepa D. and Aksenov, Alexander A. and Aleti, Gajender and Moghaddam, Jamshid Amiri and Aron, Allegra T. and Aziz, Saefuddin and Bauermeister, Anelize and Bauman, Katherine D. and Baunach, Martin and Beemelmanns, Christine and Beman, J. Michael and Berlanga-Clavero, Mar{\´i}a Victoria and Blacutt, Alex A. and Bode, Helge B. and Boullie, Anne and Brejnrod, Asker and Bugni, Tim S. and Calteau, Alexandra and Cao, Liu and Carri{\´o}n, V{\´i}ctor J. and Castelo-Branco, Raquel and Chanana, Shaurya and Chase, Alexander B. and Chevrette, Marc G. and Costa-Lotufo, Leticia V. and Crawford, Jason M. and Currie, Cameron R. and Cuypers, Bart and Dang, Tam and de Rond, Tristan and Demko, Alyssa M. and Dittmann, Elke and Du, Chao and Drozd, Christopher and Dujardin, Jean-Claude and Dutton, Rachel J. and Edlund, Anna and Fewer, David P. and Garg, Neha and Gauglitz, Julia M. and Gentry, Emily C. and Gerwick, Lena and Glukhov, Evgenia and Gross, Harald and Gugger, Muriel and Guill{\´e}n Matus, Dulce G. and Helfrich, Eric J. N. and Hempel, Benjamin-Florian and Hur, Jae-Seoun and Iorio, Marianna and Jensen, Paul R. and Kang, Kyo Bin and Kaysser, Leonard and Kelleher, Neil L. and Kim, Chung Sub and Kim, Ki Hyun and Koester, Irina and K{\"o}nig, Gabriele M. and Leao, Tiago and Lee, Seoung Rak and Lee, Yi-Yuan and Li, Xuanji and Little, Jessica C. and Maloney, Katherine N. and M{\"a}nnle, Daniel and Martin H, Christian and McAvoy, Andrew C. and Metcalf, Willam W. and Mohimani, Hosein and Molina-Santiago, Carlos and Moore, Bradley S. and Mullowney, Michael W. and Muskat, Mitchell and Nothias, Louis-F{\´e}lix and O'Neill, Ellis C. and Parkinson, Elizabeth I. and Petras, Daniel and Piel, J{\"o}rn and Pierce, Emily C. and Pires, Karine and Reher, Raphael and Romero, Diego and Roper, M. Caroline and Rust, Michael and Saad, Hamada and Saenz, Carmen and Sanchez, Laura M. and S{\o}rensen, S{\o}ren Johannes and Sosio, Margherita and S{\"u}ssmuth, Roderich D. and Sweeney, Douglas and Tahlan, Kapil and Thomson, Regan J. and Tobias, Nicholas J. and Trindade-Silva, Amaro E. and van Wezel, Gilles P. and Wang, Mingxun and Weldon, Kelly C. and Zhang, Fan and Ziemert, Nadine and Duncan, Katherine R. and Cr{\"u}semann, Max and Rogers, Simon and Dorrestein, Pieter C. and Medema, Marnix H. and van der Hooft, Justin J. J.}, title = {A community resource for paired genomic and metabolomic data mining}, series = {Nature Chemical Biology}, volume = {17}, journal = {Nature Chemical Biology}, number = {4}, publisher = {Nature}, issn = {1552-4469}, doi = {10.1038/s41589-020-00724-z}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-34708}, pages = {363 -- 368}, year = {2021}, language = {en} } @inproceedings{PaulHerder2018, author = {Paul, Felix and Herder, Jens}, title = {A model-based filtering approach for real-time human motion data}, series = {Workshop Proceedings / Tagungsband: Virtuelle und Erweiterte Realit{\"a}t - 15. Workshop der GI-Fachgruppe VR/AR}, booktitle = {Workshop Proceedings / Tagungsband: Virtuelle und Erweiterte Realit{\"a}t - 15. Workshop der GI-Fachgruppe VR/AR}, editor = {Herder, Jens and Geiger, Christian and D{\"o}rner, Ralf and Grimm, Paul}, publisher = {Shaker Verlag}, address = {Herzogenrath}, isbn = {978-3-8440-6215-1}, doi = {10.2370/9783844062151}, pages = {37 -- 44}, year = {2018}, abstract = {Acquiring human motion data from video images plays an important role in the field of computer vision. Ground truth tracking systems require markers to create high quality motion data. But in many applications it is desired to work without markers. In recent years affordable hardware for markerless tracking systems was made available at a consumer level. Efficient depth camera systems based on Time-of-Flight sensors and structured light systems have made it possible to record motion data in real time. However, the gap between the quality of marker-based and markerless systems is high. The error sources of a markerless motion tracking pipeline are discussed and a model-based filter is proposed, which adapts depending on spatial location. The proposed method is then proven to be more robust and accurate than the unfiltered data stream and can be used to visually enhance the presence of an actor within a virtual environment in live broadcast productions.}, language = {en} }