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spezial:bib [2019/08/20 22:27] Dr. Vladimir Golkov |
spezial:bib [2019/11/05 07:10] usenko |
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titleurl = {rodola-3dv14.pdf}, | titleurl = {rodola-3dv14.pdf}, | ||
topic = {Correspondence, | topic = {Correspondence, | ||
- | } | ||
- | |||
- | @Misc{demmel14, | ||
- | author = Nikolaus Demmel, | ||
- | title = Total Variation Segmentation Incorporating Depth Information | ||
- | month = Sept., | ||
- | year = 2014 | ||
- | note = IDP Project, | ||
- | keywords = Total Variation, Segmentation, | ||
} | } | ||
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year = {2019}, | year = {2019}, | ||
note = {(Presented at Symposium on Geometry Processing (SGP)) {<a href=" | note = {(Presented at Symposium on Geometry Processing (SGP)) {<a href=" | ||
+ | } | ||
+ | |||
+ | @inproceedings{sang2020wacv, | ||
+ | title = {Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach}, | ||
+ | author = {Sang, L. and Haefner, B. and Cremers, D.}, | ||
+ | booktitle={IEEE Winter Conference on Applications of Computer Vision (WACV)}, | ||
+ | month={March}, | ||
+ | address={Colorado, | ||
+ | year = {2020}, | ||
+ | award = {}, | ||
+ | titleurl = {sang2020wacv.pdf}, | ||
+ | } | ||
+ | |||
+ | @article{brahimi2019springer, | ||
+ | title = {On well-posedness of uncalibrated photometric stereo under general lighting}, | ||
+ | author = {Brahimi, M. and Quéau, Y. and Haefner, B. and Cremers, D.}, | ||
+ | journal = {{Under Review}}, | ||
+ | year = {2019}, | ||
+ | eprint = {}, | ||
+ | eprinttype = {}, | ||
+ | eprintclass = {}, | ||
+ | titleurl = {brahimi2019springer.pdf}, | ||
} | } | ||
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address={Québec City, Canada}, | address={Québec City, Canada}, | ||
year = {2019}, | year = {2019}, | ||
+ | award = {Spotlight Presentation}, | ||
titleurl = {haefner20193dv.pdf}, | titleurl = {haefner20193dv.pdf}, | ||
+ | note = { | ||
+ | {<a href="/ | ||
+ | }, | ||
} | } | ||
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note = { | note = { | ||
{<a href="/ | {<a href="/ | ||
+ | {<a href="/ | ||
{<a href=" | {<a href=" | ||
{<a href=" | {<a href=" | ||
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booktitle = ismrm, | booktitle = ismrm, | ||
keywords = {novelty detection, anomaly detection, machine learning, medical imaging, magnetic resonance imaging, diffusion MRI, segmentation}, | keywords = {novelty detection, anomaly detection, machine learning, medical imaging, magnetic resonance imaging, diffusion MRI, segmentation}, | ||
- | } | ||
- | |||
- | @inproceedings{Swazinna-et-al-ismrm2019, | ||
- | author = {P. Swazinna and V. Golkov and I. Lipp and E. Sgarlata and V. Tomassini and D. K. Jones and D. Cremers}, | ||
- | title = {Negative-Unlabeled Learning for Diffusion MRI}, | ||
- | year = {2019}, | ||
- | booktitle = ismrm, | ||
- | keywords = {deep learning, machine learning, medical imaging, magnetic resonance imaging, diffusion MRI, weakly-supervised learning, positive-unlabeled learning, semi-supervised learning, localization, | ||
} | } | ||
Line 8229: | Line 8239: | ||
eprinttype = {arXiv}, | eprinttype = {arXiv}, | ||
keywords = {deep learning, novelty detection, anomaly detection, neural networks, medical imaging, magnetic resonance imaging, diffusion MRI, deeplearning, | keywords = {deep learning, novelty detection, anomaly detection, neural networks, medical imaging, magnetic resonance imaging, diffusion MRI, deeplearning, | ||
+ | award = {Oral Presentation} | ||
+ | } | ||
+ | |||
+ | @inproceedings{Swazinna-et-al-ismrm2019, | ||
+ | author = {P. Swazinna and V. Golkov and I. Lipp and E. Sgarlata and V. Tomassini and D. K. Jones and D. Cremers}, | ||
+ | title = {Negative-Unlabeled Learning for Diffusion MRI}, | ||
+ | year = {2019}, | ||
+ | booktitle = ismrm, | ||
+ | keywords = {deep learning, machine learning, medical imaging, magnetic resonance imaging, diffusion MRI, weakly-supervised learning, positive-unlabeled learning, semi-supervised learning, localization, | ||
} | } | ||
Line 8371: | Line 8390: | ||
} | } | ||
+ | @InProceedings{schubert2019vidsors, | ||
+ | author = "D. Schubert and N. Demmel and L. von Stumberg and V. Usenko and D. Cremers", | ||
+ | title = " | ||
+ | booktitle = iros, | ||
+ | year = " | ||
+ | month = " | ||
+ | arXiv = " | ||
+ | note = {{<a href=" | ||
+ | keywords = vidsors | ||
+ | } | ||
Line 8676: | Line 8705: | ||
@article{gn-net-19, | @article{gn-net-19, | ||
author = "L. von Stumberg and P. Wenzel and Q. Khan and D. Cremers", | author = "L. von Stumberg and P. Wenzel and Q. Khan and D. Cremers", | ||
- | title = " | + | title = " |
journal = {preprint}, | journal = {preprint}, | ||
year = " | year = " | ||
- | note = {{<a href=" | + | note = {{<a href=" |
keywords = {gn-net} | keywords = {gn-net} | ||
} | } | ||
Line 8691: | Line 8720: | ||
} | } | ||
- | @article{control-across-weathers-19, | + | @inproceedings{control-across-weathers-19, |
author = "Q. Khan and P. Wenzel and D. Cremers and L. Leal-Taixe", | author = "Q. Khan and P. Wenzel and D. Cremers and L. Leal-Taixe", | ||
title = " | title = " | ||
- | | + | |
year = " | year = " | ||
note = {{<a href=" | note = {{<a href=" | ||
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eprintclass = {cs.CV}, | eprintclass = {cs.CV}, | ||
} | } | ||
+ | |||
+ | @inproceedings{jung2019corl, | ||
+ | author = {E. Jung and N. Yang and D. Cremers}, | ||
+ | booktitle = {Conference on Robot Learning (CoRL)}, | ||
+ | title = {{Multi-Frame GAN: Image Enhancement for Stereo Visual Odometry in Low Light}}, | ||
+ | award = {Full Oral Presentation}, | ||
+ | note = {{<a href=" | ||
+ | year = {2019} | ||
+ | } | ||
+ | |||
+ | @inproceedings{weiss2019sparse, | ||
+ | title={Sparse Surface Constraints for Combining Physics-based Elasticity Simulation and Correspondence-Free Object Reconstruction}, | ||
+ | author={S. Weiss and R. Maier and R. Westermann and D. Cremers and N. Thuerey}, | ||
+ | journal = {preprint}, | ||
+ | booktitle = {arXiv preprint arXiv: | ||
+ | year={2019}, | ||
+ | eprint = {1910.01812}, | ||
+ | eprinttype = {arXiv}, | ||
+ | eprintclass = {cs.CV}, | ||
+ | note = {{<a href=" | ||
+ | } | ||
+ | |||
+ | @article{Della-Libera-et-al-2019, | ||
+ | author = {L. Della Libera and V. Golkov and Y. Zhu and A. Mielke and D. Cremers}, | ||
+ | title = {Deep Learning for 2D and 3D Rotatable Data: An Overview of Methods}, | ||
+ | year = {2019}, | ||
+ | journal = {arXiv preprint arXiv: | ||
+ | eprint = {1910.14594}, | ||
+ | eprinttype = {arXiv}, | ||
+ | keywords = {deep learning, neural networks, 2D, 3D, rotations, invariance, equivariance, | ||
+ | } | ||
+ | |||
+ |