Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Eleonora Vig"'
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision (ICCV).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f5f0410993677a0dbbf3cf03ed7e2417
https://doi.org/10.1109/iccv48922.2021.01193
https://doi.org/10.1109/iccv48922.2021.01193
Publikováno v:
ICPR
The availability of large-scale annotated datasets has enabled Fully-Convolutional Neural Networks to reach outstanding performance on road extraction in aerial images. However, high-quality pixel-level annotation is expensive to produce and even man
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cfed26f78156efc1728bc3321f3d4357
https://doi.org/10.1109/icpr48806.2021.9412054
https://doi.org/10.1109/icpr48806.2021.9412054
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-1, Pp 19-23 (2018)
High-resolution aerial imagery can provide detailed and in some cases even real-time information about traffic related objects. Vehicle localization and counting using aerial imagery play an important role in a broad range of applications. Recently,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8be245b16e38f6204f414d1606be9735
https://doi.org/10.5194/isprs-archives-xlii-1-19-2018
https://doi.org/10.5194/isprs-archives-xlii-1-19-2018
Publikováno v:
ICCV
Understanding the complex urban infrastructure with centimeter-level accuracy is essential for many applications from autonomous driving to mapping, infrastructure monitoring, and urban management. Aerial images provide valuable information over a la
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::858e0a810244d69a5fa0cfc8d382f817
https://elib.dlr.de/131251/
https://elib.dlr.de/131251/
Autor:
Veronika Gstaiger, Franz Kurz, Seyed Majid Azimi, Stefan Auer, Corentin Henry, Sascha Knake-Langhorst, P. Pekezou-Fouopi, Nina Merkle, Dominik Rosenbaum, Eleonora Vig, D. Waigand, Peter Reinartz
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-1, Pp 251-256 (2018)
DLRAD – a new vision and mapping benchmark dataset for autonomous driving is under development for the validation of intelligent driving algorithms. Stationary, mobile, and airborne sensors monitored simultaneously the environment around a referenc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5159b2157062000bf959454afa0fbf1f
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-1/251/2018/
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-1/251/2018/
Publikováno v:
Computer Vision – ACCV 2018 ISBN: 9783030208929
ACCV (3)
ACCV (3)
Automatic multi-class object detection in remote sensing images in unconstrained scenarios is of high interest for several applications including traffic monitoring and disaster management. The huge variation in object scale, orientation, category, a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3280acb09f495535e49f7cb942ac32b1
http://arxiv.org/abs/1807.02700
http://arxiv.org/abs/1807.02700
Autor:
Peter Reinartz, Corentin Henry, Kevin Alonso, Eleonora Vig, Ksenia Bittner, Emiliano Carmona, Jiaojiao Tian, Rupert Müller, Reza Bahmanyar, Franz Kurz, Daniele Cerra, Seyed Majid Azimi, Miguel Pato, Peter Fischer, Pablo d'Angelo
Publikováno v:
IGARSS
This article describes the workflow of the classification algorithm which ranked at 2nd place in the 2018 GRSS Data Fusion Contest. The objective of the contest was to provide a classification map with 20 classes on a complex urban scenario. The avai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b310af73e8eb730804ab5e27fc5dc685
https://elib.dlr.de/120750/
https://elib.dlr.de/120750/
Autor:
Eleonora Vig, Michael Dorr
Publikováno v:
Visual Content Indexing and Retrieval with Psycho-Visual Models ISBN: 9783319576862
Visual Content Indexing and Retrieval with Psycho-Visual Models
Visual Content Indexing and Retrieval with Psycho-Visual Models
Despite all recent progress in computer vision, humans are still far superior to machines when it comes to the high-level understanding of complex dynamic scenes. The apparent ease of human perception and action cannot be explained by sheer neural co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7ee618ca64e657b469fcc288256cc1a8
https://doi.org/10.1007/978-3-319-57687-9_5
https://doi.org/10.1007/978-3-319-57687-9_5
Publikováno v:
Journal of Field Robotics. 31:780-802
For robots operating in outdoor environments, a number of factors, including weather, time of day, rough terrain, high speeds, and hardware limitations, make performing vision-based simultaneous localization and mapping with current techniques infeas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::344048f76680bcf00f5dcaaaca592ba8
https://doi.org/10.1002/rob.21532
https://doi.org/10.1002/rob.21532
Publikováno v:
CVPR
Modern computer vision algorithms typically require expensive data acquisition and accurate manual labeling. In this work, we instead leverage the recent progress in computer graphics to generate fully labeled, dynamic, and photo-realistic proxy virt
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3e988e41e1e7c215ccd346cad9bbfc6a
https://doi.org/10.1109/cvpr.2016.470
https://doi.org/10.1109/cvpr.2016.470