Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Raffaele Imbriaco"'
Publikováno v:
Remote Sensing, Vol 11, Iss 5, p 493 (2019)
Remote Sensing Image Retrieval remains a challenging topic due to the special nature of Remote Sensing imagery. Such images contain various different semantic objects, which clearly complicates the retrieval task. In this paper, we present an image r
Externí odkaz:
https://doaj.org/article/c331352938e04db591e099980ebc0825
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing, 60:9491804. Institute of Electrical and Electronics Engineers
The availability of large-scale remote sensing (RS) data facilitates a wide range of applications, such as disaster management and urban planning. An approach for such problems is image retrieval, where, given a query image, the goal is to find the m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9dcbd5ad6731dcddf6733a849fa33499
https://doi.org/10.1109/tgrs.2021.3095957
https://doi.org/10.1109/tgrs.2021.3095957
Autor:
Clint Sebastian, Raffaele Imbriaco, Panagiotis Meletis, Gijs Dubbelman, Egor Bondarev, Peter H.N. de With
Publikováno v:
CVPR Workshops
Proceedings-2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021, 4133-4141
STARTPAGE=4133;ENDPAGE=4141;TITLE=Proceedings-2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
Proceedings-2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021, 4133-4141
STARTPAGE=4133;ENDPAGE=4141;TITLE=Proceedings-2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
Retrieving specific vehicle tracks by Natural Language (NL)-based descriptions is a convenient way to monitor vehicle movement patterns and traffic-related events. NL-based image retrieval has several applications in smart cities, traffic control, et
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::938d5023650d18cb0772617b36327a23
https://research.tue.nl/en/publications/b665cba2-a052-45ec-a980-77fdf1f8d746
https://research.tue.nl/en/publications/b665cba2-a052-45ec-a980-77fdf1f8d746
Publikováno v:
Proceedings-2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020, 2475-2484
STARTPAGE=2475;ENDPAGE=2484;TITLE=Proceedings-2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
CVPR Workshops
STARTPAGE=2475;ENDPAGE=2484;TITLE=Proceedings-2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
CVPR Workshops
Vehicle re-identification plays a crucial role in the management of transportation infrastructure and traffic flow. However, this is a challenging task due to the large view-point variations in appearance, environmental and instance-related factors.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4f9daf839e374ac77daecdf41c76ca68
https://research.tue.nl/nl/publications/b115206b-385c-4f28-8caf-7da841e0019a
https://research.tue.nl/nl/publications/b115206b-385c-4f28-8caf-7da841e0019a
Publikováno v:
Image Processing: Algorithms and Systems
Visual place recognition using query and database images from different sources remains a challenging task in computer vision. Our method exploits global descriptors for efficient image matching and local descriptors for geometric verification. We pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::57c8238b7df84f9a04c10d84a25f8935
https://doi.org/10.2352/ISSN.2470-1173.2020.10.IPAS-313
https://doi.org/10.2352/ISSN.2470-1173.2020.10.IPAS-313
Publikováno v:
VISIGRAPP (5: VISAPP)
Context-based remote sensing image retrieval (CBRSIR) is an important problem in computer vision with many applications such as military, agriculture, and surveillance. In this study, inspired by recent developments in person re-identification, we de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::81d3de489b12c15ac13e4ed2bed2f0a4
https://doi.org/10.5220/0008895702420249
https://doi.org/10.5220/0008895702420249