Zobrazeno 1 - 10
of 65
pro vyhledávání: '"Vig, Eleonora"'
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:
http://arxiv.org/abs/2007.06102
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:
http://arxiv.org/abs/1807.02700
Publikováno v:
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 2016
Most saliency estimation methods aim to explicitly model low-level conspicuity cues such as edges or blobs and may additionally incorporate top-down cues using face or text detection. Data-driven methods for training saliency models using eye-fixatio
Externí odkaz:
http://arxiv.org/abs/1804.01793
Action recognition in videos is a challenging task due to the complexity of the spatio-temporal patterns to model and the difficulty to acquire and learn on large quantities of video data. Deep learning, although a breakthrough for image classificati
Externí odkaz:
http://arxiv.org/abs/1608.07138
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:
http://arxiv.org/abs/1605.06457
Autor:
Gaidon, Adrien, Vig, Eleonora
Automatically detecting, labeling, and tracking objects in videos depends first and foremost on accurate category-level object detectors. These might, however, not always be available in practice, as acquiring high-quality large scale labeled trainin
Externí odkaz:
http://arxiv.org/abs/1508.00776
In spite of the many advantages of aerial imagery for crowd monitoring and management at mass events, datasets of aerial images of crowds are still lacking in the field. As a remedy, in this work we introduce a novel crowd dataset, the DLR Aerial Cro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::42e7488e6c40981f9d01d3acddeaf051
http://arxiv.org/abs/1909.12743
http://arxiv.org/abs/1909.12743
Autor:
Kurz, Franz, Waigand, Daniel, Pekezou Fouopi, Paulin, Vig, Eleonora, Henry, Corentin, Merkle, Nina, Rosenbaum, Dominik, Gstaiger, Veronika, Azimi, Seyedmajid, Auer, Stefan, Reinartz, Peter, Knake-Langhorst, Sascha
DLRAD - a new vision and mapping benchmark dataset for autonomous driving is now ready for the development and validation of intelligent driving algorithms. Stationary, mobile, and airborne sensors monitored simultaneously the environment around a re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1640::f39d6393d7f284719c0c82b170a50ce5
https://elib.dlr.de/120498/
https://elib.dlr.de/120498/
Autor:
Dorr, Michael1,2 (AUTHOR) michael.dorr@schepens.harvard.edu, Vig, Eleonora1 (AUTHOR), Barth, Erhardt1 (AUTHOR)
Publikováno v:
Visual Cognition. Apr2012, Vol. 20 Issue 4/5, p495-514. 20p. 1 Color Photograph, 1 Chart, 3 Graphs.
