Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Moonen, Steven"'
Publikováno v:
BMVC 2022
Recently, the use of synthetic training data has been on the rise as it offers correctly labelled datasets at a lower cost. The downside of this technique is that the so-called domain gap between the real target images and synthetic training data lea
Externí odkaz:
http://arxiv.org/abs/2211.16066
Autor:
Moonen, Steven, Vanherle, Bram, de Hoog, Joris, Bourgana, Taoufik, Bey-Temsamani, Abdellatif, Michiels, Nick
The use of computer vision for product and assembly quality control is becoming ubiquitous in the manufacturing industry. Lately, it is apparent that machine learning based solutions are outperforming classical computer vision algorithms in terms of
Externí odkaz:
http://arxiv.org/abs/2211.14054
We present a diverse dataset of industrial metal objects. These objects are symmetric, textureless and highly reflective, leading to challenging conditions not captured in existing datasets. Our dataset contains both real-world and synthetic multi-vi
Externí odkaz:
http://arxiv.org/abs/2208.04052
Akademický článek
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Autor:
Steven Moonen, Bram Vanherle, Joris de Hoog, Taoufik Bourgana, Abdellatif Bey-Temsamani, Nick Michiels
The use of computer vision for product and assembly quality control is becoming ubiquitous in the manufacturing industry. Lately, it is apparent that machine learning based solutions are outperforming classical computer vision algorithms in terms of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1c3490be032abc697f153e6bba9519b0
http://hdl.handle.net/1942/39648
http://hdl.handle.net/1942/39648
Publikováno v:
Nick MICHIELS
Recently, the use of synthetic training data has been on the rise as it offers correctly labelled datasets at a lower cost. The downside of this technique is that the so-called domain gap between the real target images and synthetic training data lea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8c955a58c7c78eb37bda8f2578701880
http://arxiv.org/abs/2211.16066
http://arxiv.org/abs/2211.16066