Zobrazeno 1 - 10
of 330
pro vyhledávání: '"Ortmaier, Tobias"'
Autor:
Ehlers, Simon F. G., Ziaukas, Zygimantas, Kobler, Jan-Philipp, Busch, Alexander, Ortmaier, Tobias, Wielitzka, Mark
As part of the automation of commercial vehicles, the number of assistance systems in this field is continuously increasing. The semitrailer plays an important role for the vehicles driving dynamics due to its highly varying loads and the large propo
Externí odkaz:
http://arxiv.org/abs/2407.10270
Parallel robots (PRs) offer the potential for safe human-robot collaboration because of their low moving masses. Due to the in-parallel kinematic chains, the risk of contact in the form of collisions and clamping at a chain increases. Ensuring safety
Externí odkaz:
http://arxiv.org/abs/2308.09656
Parallel robots (PRs) allow for higher speeds in human-robot collaboration due to their lower moving masses but are more prone to unintended contact. For a safe reaction, knowledge of the location and force of a collision is useful. A novel algorithm
Externí odkaz:
http://arxiv.org/abs/2308.09650
Parallel robots provide the potential to be leveraged for human-robot collaboration (HRC) due to low collision energies even at high speeds resulting from their reduced moving masses. However, the risk of unintended contact with the leg chains increa
Externí odkaz:
http://arxiv.org/abs/2308.09633
The consideration of predictive uncertainty in medical imaging with deep learning is of utmost importance. We apply estimation of both aleatoric and epistemic uncertainty by variational Bayesian inference with Monte Carlo dropout to regression tasks
Externí odkaz:
http://arxiv.org/abs/2104.12376
Uncertainty quantification in inverse medical imaging tasks with deep learning has received little attention. However, deep models trained on large data sets tend to hallucinate and create artifacts in the reconstructed output that are not anatomical
Externí odkaz:
http://arxiv.org/abs/2008.08837
Patient-Specific Domain Adaptation for Fast Optical Flow Based on Teacher-Student Knowledge Transfer
Fast motion feedback is crucial in computer-aided surgery (CAS) on moving tissue. Image-assistance in safety-critical vision applications requires a dense tracking of tissue motion. This can be done using optical flow (OF). Accurate motion prediction
Externí odkaz:
http://arxiv.org/abs/2007.04928
The model uncertainty obtained by variational Bayesian inference with Monte Carlo dropout is prone to miscalibration. In this paper, different logit scaling methods are extended to dropout variational inference to recalibrate model uncertainty. Expec
Externí odkaz:
http://arxiv.org/abs/2006.11584
Model uncertainty obtained by variational Bayesian inference with Monte Carlo dropout is prone to miscalibration. The uncertainty does not represent the model error well. In this paper, temperature scaling is extended to dropout variational inference
Externí odkaz:
http://arxiv.org/abs/1909.13550
We evaluate two different methods for the integration of prediction uncertainty into diagnostic image classifiers to increase patient safety in deep learning. In the first method, Monte Carlo sampling is applied with dropout at test time to get a pos
Externí odkaz:
http://arxiv.org/abs/1908.00792