Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Marco Rudolph"'
Autor:
Lutz M. K. Krause, Emily Manderfeld, Patricia Gnutt, Louisa Vogler, Ann Wassick, Kailey Richard, Marco Rudolph, Kelli Z. Hunsucker, Geoffrey W. Swain, Bodo Rosenhahn, Axel Rosenhahn
Biofouling is a major challenge for sustainable shipping, filter membranes, heat exchangers, and medical devices. The development of fouling-resistant coatings requires the evaluation of their effectiveness. Such an evaluation is usually based on the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1a9c2bfec0085f0ec2c90513ff1de39b
http://arxiv.org/abs/2211.11607
http://arxiv.org/abs/2211.11607
Industrial defect detection is commonly addressed with anomaly detection (AD) methods where no or only incomplete data of potentially occurring defects is available. This work discovers previously unknown problems of student-teacher approaches for AD
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a5d75546097fe1e18049aee8145b02b
Publikováno v:
WACV
The detection of manufacturing errors is crucial in fabrication processes to ensure product quality and safety standards. Since many defects occur very rarely and their characteristics are mostly unknown a priori, their detection is still an open res
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8abf37714001ba2f0571ff0856a6a27b
https://doi.org/10.1109/wacv48630.2021.00195
https://doi.org/10.1109/wacv48630.2021.00195
Publikováno v:
CVPR
Human pose estimation from single images is a challenging problem in computer vision that requires large amounts of labeled training data to be solved accurately. Unfortunately, for many human activities (e.g. outdoor sports) such training data does
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e41640fbd9d2d72498388ad8f7051001
Publikováno v:
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
In this paper we propose Structuring AutoEncoders (SAE). SAEs are neural networks which learn a low dimensional representation of data and are additionally enriched with a desired structure in this low dimensional space. While traditional Autoencoder
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::56507d88b160f7580fcbb5661d8f8683
https://doi.org/10.1109/iccvw.2019.00075
https://doi.org/10.1109/iccvw.2019.00075