| Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges D Feng, C Haase-Schuetz, L Rosenbaum, H Hertlein, C Glaeser, F Timm, ... https://arxiv.org/abs/1902.07830v2, 2019 | 1309 | 2019 |
| Accurate eye centre localisation by means of gradients. F Timm, E Barth Visapp 11, 125-130, 2011 | 475 | 2011 |
| Non-parametric texture defect detection using Weibull features F Timm, E Barth Image Processing: Machine Vision Applications IV 7877, 150-161, 2011 | 83 | 2011 |
| Leveraging heteroscedastic aleatoric uncertainties for robust real-time lidar 3d object detection D Feng, L Rosenbaum, F Timm, K Dietmayer 2019 IEEE Intelligent Vehicles Symposium (IV), 1280-1287, 2019 | 81 | 2019 |
| Can we trust you? on calibration of a probabilistic object detector for autonomous driving D Feng, L Rosenbaum, C Glaeser, F Timm, K Dietmayer arXiv preprint arXiv:1909.12358, 2019 | 47 | 2019 |
| DeepReflecs: Deep Learning for Automotive Object Classification with Radar Reflections M Ulrich, C Gläser, F Timm https://arxiv.org/abs/2010.09273, 2020 | 33 | 2020 |
| Inferring spatial uncertainty in object detection Z Wang, D Feng, Y Zhou, L Rosenbaum, F Timm, K Dietmayer, ... 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020 | 32 | 2020 |
| Leveraging uncertainties for deep multi-modal object detection in autonomous driving D Feng, Y Cao, L Rosenbaum, F Timm, K Dietmayer 2020 IEEE Intelligent Vehicles Symposium (IV), 877-884, 2020 | 31 | 2020 |
| Holistic filter pruning for efficient deep neural networks L Enderich, F Timm, W Burgard Proceedings of the IEEE/CVF winter conference on applications of computer …, 2021 | 27 | 2021 |
| Labels are not perfect: Inferring spatial uncertainty in object detection D Feng, Z Wang, Y Zhou, L Rosenbaum, F Timm, K Dietmayer, ... IEEE Transactions on Intelligent Transportation Systems 23 (8), 9981-9994, 2021 | 22 | 2021 |
| Novelty detection for the inspection of light-emitting diodes F Timm, E Barth Expert Systems with Applications 39 (3), 3413-3422, 2012 | 21 | 2012 |
| Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets D Feng, C Haase-Schuetz, L Rosenbaum, H Hertlein, C Gläser, F Timm, ... Methods, and Challenges, 2019 | 18 | 2019 |
| Statistical Fourier Descriptors for Defect Image Classification F Timm, T Martinetz 20th Int. Conference on Pattern Recognition (ICPR), 4190-4193, 2010 | 14* | 2010 |
| SYMOG: learning symmetric mixture of Gaussian modes for improved fixed-point quantization L Enderich, F Timm, W Burgard https://arxiv.org/abs/2002.08204, 2020 | 9 | 2020 |
| Learning Multimodal Fixed-Point Weights using Gradient Descent L Enderich, F Timm, L Rosenbaum, W Burgard 7th European Symposium on Artificial Neural Networks …, 2019 | 9 | 2019 |
| Method and apparatus for estimating a pose T Martinetz, K Ehlers, F Timm, E Barth, S Klement US Patent 9,159,134, 2015 | 9 | 2015 |
| Simple incremental one-class support vector classification K Labusch, F Timm, T Martinetz Joint Pattern Recognition Symposium, 21-30, 2008 | 9 | 2008 |
| Where can i drive? a system approach: Deep ego corridor estimation for robust automated driving T Michalke, C Wüst, D Feng, M Dolgov, C Gläser, F Timm 2021 IEEE International Intelligent Transportation Systems Conference (ITSC …, 2021 | 8 | 2021 |
| Labels are not perfect: Improving probabilistic object detection via label uncertainty D Feng, L Rosenbaum, F Timm, K Dietmayer arXiv preprint arXiv:2008.04168, 2020 | 7 | 2020 |
| Welding Inspection using Novel Specularity Features and a One-class SVM. F Timm, S Klement, T Martinetz, E Barth Int. Conference on Computer Theory and Applications (VISAPP) 1 (INSTICC …, 2009 | 7 | 2009 |
Lars RosenbaumRobert Bosch GmbHVerified email at de.bosch.com
