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pro vyhledávání: '"Nassar, Ahmed Samy"'
In this paper we propose an end-to-end learnable approach that detects static urban objects from multiple views, re-identifies instances, and finally assigns a geographic position per object. Our method relies on a Graph Neural Network (GNN) to, dete
Externí odkaz:
http://arxiv.org/abs/2003.10151
We propose to jointly learn multi-view geometry and warping between views of the same object instances for robust cross-view object detection. What makes multi-view object instance detection difficult are strong changes in viewpoint, lighting conditi
Externí odkaz:
http://arxiv.org/abs/1907.10892
Publikováno v:
Proceedings of the IEEE, 105, pp. 1884-1899, 2017
In this paper, we discuss and review how combined multi-view imagery from satellite to street-level can benefit scene analysis. Numerous works exist that merge information from remote sensing and images acquired from the ground for tasks like land co
Externí odkaz:
http://arxiv.org/abs/1705.08101
Autor:
Nassar, Ahmed Samy
Publikováno v:
Computer Vision and Pattern Recognition [cs.CV]. Université de Bretagne Sud, 2021. English. ⟨NNT : 2021LORIS595⟩
Creating inventories of street-side objects and their monitoring in cities is a labor-intensive and costly process. Field workers are known to conduct this process on-site to record properties about the object. These properties can be the location, s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::0ca65fe5b16f528e03985a905b479fa4
https://theses.hal.science/tel-03523658
https://theses.hal.science/tel-03523658
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