@inproceedings{GolkovDosovitskiySaemannetal.2015, author = {Golkov, Vladimir and Dosovitskiy, Alexey and S{\"a}mann, Philipp G. and Sperl, Jonathan I. and Sprenger, Tim and Czisch, Michael and Menzel, Marion Irene and G{\´o}mez, Pedro A. and Haase, Axel and Brox, Thomas and Cremers, Daniel}, title = {q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans}, booktitle = {Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I}, editor = {Navab, Nassir and Hornegger, Joachim and Wells, William M. and Frangi, Alejandro F.}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-24553-9}, doi = {https://doi.org/10.1007/978-3-319-24553-9_5}, pages = {37 -- 44}, year = {2015}, language = {en} } @inproceedings{GolkovSprengerSperletal.2016, author = {Golkov, Vladimir and Sprenger, Tim and Sperl, Jonathan I. and Menzel, Marion Irene and Czisch, Michael and S{\"a}mann, Philipp G. and Cremers, Daniel}, title = {Model-free novelty-based diffusion MRI}, booktitle = {2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-4799-2349-6}, doi = {https://doi.org/10.1109/ISBI.2016.7493489}, pages = {1233 -- 1236}, year = {2016}, language = {en} } @article{GolkovDosovitskiySperletal.2016, author = {Golkov, Vladimir and Dosovitskiy, Alexey and Sperl, Jonathan I. and Menzel, Marion Irene and Czisch, Michael and S{\"a}mann, Philipp G. and Brox, Thomas and Cremers, Daniel}, title = {q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans}, volume = {35}, journal = {IEEE Transactions on Medical Imaging}, number = {5}, publisher = {IEEE}, address = {New York}, issn = {1558-254X}, doi = {https://doi.org/10.1109/TMI.2016.2551324}, pages = {1344 -- 1351}, year = {2016}, language = {en} } @inproceedings{GolkovSperlMenzeletal.2014, author = {Golkov, Vladimir and Sperl, Jonathan I. and Menzel, Marion Irene and Sprenger, Tim and Tan, Ek Tsoon and Marinelli, Luca and Hardy, Christopher J. and Haase, Axel and Cremers, Daniel}, title = {Joint Super-Resolution Using Only One Anisotropic Low-Resolution Image per q-Space Coordinate}, booktitle = {Computational Diffusion MRI, MICCAI Workshop, Boston, MA, USA, September 2014}, editor = {O'Donnell, Lauren and Nedjati-Gilan, Gemma and Rathi, Yogesh and Reisert, Marco and Schneider, Torben}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-11182-7}, doi = {https://doi.org/10.1007/978-3-319-11182-7_16}, pages = {181 -- 191}, year = {2014}, language = {en} } @inproceedings{GernerRoessleCremersetal.2024, author = {Gerner, Jeremias and R{\"o}ssle, Dominik and Cremers, Daniel and Bogenberger, Klaus and Sch{\"o}n, Torsten and Schmidtner, Stefanie}, title = {Enhancing Realistic Floating Car Observers in Microscopic Traffic Simulation}, booktitle = {2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-9946-2}, doi = {https://doi.org/10.1109/ITSC57777.2023.10422398}, pages = {2396 -- 2403}, year = {2024}, language = {en} } @unpublished{RoessleGernerBogenbergeretal.2024, author = {R{\"o}ßle, Dominik and Gerner, Jeremias and Bogenberger, Klaus and Cremers, Daniel and Schmidtner, Stefanie and Sch{\"o}n, Torsten}, title = {Unlocking Past Information: Temporal Embeddings in Cooperative Bird's Eye View Prediction}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2401.14325}, year = {2024}, abstract = {Accurate and comprehensive semantic segmentation of Bird's Eye View (BEV) is essential for ensuring safe and proactive navigation in autonomous driving. Although cooperative perception has exceeded the detection capabilities of single-agent systems, prevalent camera-based algorithms in cooperative perception neglect valuable information derived from historical observations. This limitation becomes critical during sensor failures or communication issues as cooperative perception reverts to single-agent perception, leading to degraded performance and incomplete BEV segmentation maps. This paper introduces TempCoBEV, a temporal module designed to incorporate historical cues into current observations, thereby improving the quality and reliability of BEV map segmentations. We propose an importance-guided attention architecture to effectively integrate temporal information that prioritizes relevant properties for BEV map segmentation. TempCoBEV is an independent temporal module that seamlessly integrates into state-of-the-art camera-based cooperative perception models. We demonstrate through extensive experiments on the OPV2V dataset that TempCoBEV performs better than non-temporal models in predicting current and future BEV map segmentations, particularly in scenarios involving communication failures. We show the efficacy of TempCoBEV and its capability to integrate historical cues into the current BEV map, improving predictions under optimal communication conditions by up to 2\% and under communication failures by up to 19\%. The code will be published on GitHub.}, language = {en} } @inproceedings{SouzadeAssisRoessleetal.2023, author = {Souza, Bruno J. and de Assis, Lucas C. and R{\"o}ßle, Dominik and Freire, Roberto Z. and Cremers, Daniel and Sch{\"o}n, Torsten and Georges, Munir}, title = {AImotion Challenge Results: a Framework for AirSim Autonomous Vehicles and Motion Replication}, booktitle = {2022 2nd International Conference on Computers and Automation (CompAuto 2022): Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-8194-6}, doi = {https://doi.org/10.1109/CompAuto55930.2022.00015}, pages = {42 -- 47}, year = {2023}, language = {en} } @article{RoesslePreyRamgraberetal.2023, author = {R{\"o}ßle, Dominik and Prey, Lukas and Ramgraber, Ludwig and Hanemann, Anja and Cremers, Daniel and Noack, Patrick Ole and Sch{\"o}n, Torsten}, title = {Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset}, volume = {5}, pages = {68}, journal = {Plant Phenomics}, publisher = {American Association for the Advancement of Science (AAAS)}, address = {Washington}, issn = {2643-6515}, doi = {https://doi.org/10.34133/plantphenomics.0068}, year = {2023}, abstract = {Fusarium head blight (FHB) is one of the most prevalent wheat diseases, causing substantial yield losses and health risks. Efficient phenotyping of FHB is crucial for accelerating resistance breeding, but currently used methods are time-consuming and expensive. The present article suggests a noninvasive classification model for FHB severity estimation using red-green-blue (RGB) images, without requiring extensive preprocessing. The model accepts images taken from consumer-grade, low-cost RGB cameras and classifies the FHB severity into 6 ordinal levels. In addition, we introduce a novel dataset consisting of around 3,000 images from 3 different years (2020, 2021, and 2022) and 2 FHB severity assessments per image from independent raters. We used a pretrained EfficientNet (size b0), redesigned as a regression model. The results demonstrate that the interrater reliability (Cohen's kappa, κ) is substantially lower than the achieved individual network-to-rater results, e.g., 0.68 and 0.76 for the data captured in 2020, respectively. The model shows a generalization effect when trained with data from multiple years and tested on data from an independent year. Thus, using the images from 2020 and 2021 for training and 2022 for testing, we improved the Fw1 score by 0.14, the accuracy by 0.11, κ by 0.12, and reduced the root mean squared error by 0.5 compared to the best network trained only on a single year's data. The proposed lightweight model and methods could be deployed on mobile devices to automatically and objectively assess FHB severity with images from low-cost RGB cameras. The source code and the dataset are available at https://github.com/cvims/FHB_classification.}, language = {en} } @inproceedings{RoessleCremersSchoen2022, author = {R{\"o}ßle, Dominik and Cremers, Daniel and Sch{\"o}n, Torsten}, title = {Perceiver Hopfield Pooling for Dynamic Multi-modal and Multi-instance Fusion}, booktitle = {Artificial Neural Networks and Machine Learning - ICANN 2022: 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6-9, 2022, Proceedings, Part I}, editor = {Pimenidis, Elias and Angelov, Plamen and Jayne, Chrisina and Papaleonidas, Antonios and Aydin, Mehmet}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-15918-3}, doi = {https://doi.org/10.1007/978-3-031-15919-0_50}, pages = {599 -- 610}, year = {2022}, language = {en} } @inproceedings{WenzelSchoenLealTaixeetal.2021, author = {Wenzel, Patrick and Sch{\"o}n, Torsten and Leal-Taix{\´e}, Laura and Cremers, Daniel}, title = {Vision-based mobile robotics obstacle avoidance with deep reinforcement learning}, booktitle = {2021 IEEE International Conference on Robotics and Automation (ICRA)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-9077-8}, issn = {2577-087X}, doi = {https://doi.org/10.1109/ICRA48506.2021.9560787}, pages = {14360 -- 14366}, year = {2021}, language = {en} }