TY - CHAP A1 - Golkov, Vladimir A1 - Dosovitskiy, Alexey A1 - Sämann, Philipp G. A1 - Sperl, Jonathan I. A1 - Sprenger, Tim A1 - Czisch, Michael A1 - Menzel, Marion Irene A1 - Gómez, Pedro A. A1 - Haase, Axel A1 - Brox, Thomas A1 - Cremers, Daniel ED - Navab, Nassir ED - Hornegger, Joachim ED - Wells, William M. ED - Frangi, Alejandro F. T1 - q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans T2 - Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part I UR - https://doi.org/10.1007/978-3-319-24553-9_5 Y1 - 2015 UR - https://doi.org/10.1007/978-3-319-24553-9_5 SN - 978-3-319-24553-9 SN - 978-3-319-24552-2 SP - 37 EP - 44 PB - Springer CY - Cham ER - TY - CHAP A1 - Golkov, Vladimir A1 - Sprenger, Tim A1 - Sperl, Jonathan I. A1 - Menzel, Marion Irene A1 - Czisch, Michael A1 - Sämann, Philipp G. A1 - Cremers, Daniel T1 - Model-free novelty-based diffusion MRI T2 - 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) UR - https://doi.org/10.1109/ISBI.2016.7493489 Y1 - 2016 UR - https://doi.org/10.1109/ISBI.2016.7493489 SN - 978-1-4799-2349-6 SP - 1233 EP - 1236 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Golkov, Vladimir A1 - Dosovitskiy, Alexey A1 - Sperl, Jonathan I. A1 - Menzel, Marion Irene A1 - Czisch, Michael A1 - Sämann, Philipp G. A1 - Brox, Thomas A1 - Cremers, Daniel T1 - q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans JF - IEEE Transactions on Medical Imaging UR - https://doi.org/10.1109/TMI.2016.2551324 Y1 - 2016 UR - https://doi.org/10.1109/TMI.2016.2551324 SN - 1558-254X SN - 0278-0062 VL - 35 IS - 5 SP - 1344 EP - 1351 PB - IEEE CY - New York ER - TY - CHAP A1 - Golkov, Vladimir A1 - Sperl, Jonathan I. A1 - Menzel, Marion Irene A1 - Sprenger, Tim A1 - Tan, Ek Tsoon A1 - Marinelli, Luca A1 - Hardy, Christopher J. A1 - Haase, Axel A1 - Cremers, Daniel ED - O'Donnell, Lauren ED - Nedjati-Gilan, Gemma ED - Rathi, Yogesh ED - Reisert, Marco ED - Schneider, Torben T1 - Joint Super-Resolution Using Only One Anisotropic Low-Resolution Image per q-Space Coordinate T2 - Computational Diffusion MRI, MICCAI Workshop, Boston, MA, USA, September 2014 UR - https://doi.org/10.1007/978-3-319-11182-7_16 Y1 - 2014 UR - https://doi.org/10.1007/978-3-319-11182-7_16 SN - 978-3-319-11182-7 SN - 978-3-319-11181-0 SP - 181 EP - 191 PB - Springer CY - Cham ER - TY - CHAP A1 - Gerner, Jeremias A1 - Rössle, Dominik A1 - Cremers, Daniel A1 - Bogenberger, Klaus A1 - Schön, Torsten A1 - Schmidtner, Stefanie T1 - Enhancing Realistic Floating Car Observers in Microscopic Traffic Simulation T2 - 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC57777.2023.10422398 Y1 - 2024 UR - https://doi.org/10.1109/ITSC57777.2023.10422398 SN - 979-8-3503-9946-2 SP - 2396 EP - 2403 PB - IEEE CY - Piscataway ER - TY - INPR A1 - Rößle, Dominik A1 - Gerner, Jeremias A1 - Bogenberger, Klaus A1 - Cremers, Daniel A1 - Schmidtner, Stefanie A1 - Schön, Torsten T1 - Unlocking Past Information: Temporal Embeddings in Cooperative Bird’s Eye View Prediction N2 - 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. UR - https://doi.org/10.48550/arXiv.2401.14325 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2401.14325 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-46123 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Souza, Bruno J. A1 - de Assis, Lucas C. A1 - Rößle, Dominik A1 - Freire, Roberto Z. A1 - Cremers, Daniel A1 - Schön, Torsten A1 - Georges, Munir T1 - AImotion Challenge Results: a Framework for AirSim Autonomous Vehicles and Motion Replication T2 - 2022 2nd International Conference on Computers and Automation (CompAuto 2022): Proceedings UR - https://doi.org/10.1109/CompAuto55930.2022.00015 KW - framework KW - automous vehicle KW - computational intelligence KW - TurtleBot KW - AirSim Y1 - 2023 UR - https://doi.org/10.1109/CompAuto55930.2022.00015 SN - 978-1-6654-8194-6 SP - 42 EP - 47 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Rößle, Dominik A1 - Prey, Lukas A1 - Ramgraber, Ludwig A1 - Hanemann, Anja A1 - Cremers, Daniel A1 - Noack, Patrick Ole A1 - Schön, Torsten T1 - Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset JF - Plant Phenomics N2 - 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. UR - https://doi.org/10.34133/plantphenomics.0068 Y1 - 2023 UR - https://doi.org/10.34133/plantphenomics.0068 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-40853 SN - 2643-6515 VL - 5 PB - American Association for the Advancement of Science (AAAS) CY - Washington ER - TY - CHAP A1 - Rößle, Dominik A1 - Cremers, Daniel A1 - Schön, Torsten ED - Pimenidis, Elias ED - Angelov, Plamen ED - Jayne, Chrisina ED - Papaleonidas, Antonios ED - Aydin, Mehmet T1 - Perceiver Hopfield Pooling for Dynamic Multi-modal and Multi-instance Fusion T2 - Artificial Neural Networks and Machine Learning – ICANN 2022: 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part I UR - https://doi.org/10.1007/978-3-031-15919-0_50 KW - perceiver KW - hopfield pooling KW - attention KW - data fusion KW - multi-modal KW - multi-instance Y1 - 2022 UR - https://doi.org/10.1007/978-3-031-15919-0_50 SN - 978-3-031-15918-3 SN - 978-3-031-15919-0 SP - 599 EP - 610 PB - Springer CY - Cham ER - TY - CHAP A1 - Wenzel, Patrick A1 - Schön, Torsten A1 - Leal-Taixé, Laura A1 - Cremers, Daniel T1 - Vision-based mobile robotics obstacle avoidance with deep reinforcement learning T2 - 2021 IEEE International Conference on Robotics and Automation (ICRA) UR - https://doi.org/10.1109/ICRA48506.2021.9560787 Y1 - 2021 UR - https://doi.org/10.1109/ICRA48506.2021.9560787 SN - 978-1-7281-9077-8 SN - 2577-087X SP - 14360 EP - 14366 PB - IEEE CY - Piscataway ER -