Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Henry O. Velesaca"'
Publikováno v:
Artificial Intelligence in Agriculture, Vol 9, Iss , Pp 89-99 (2023)
This article presents an efficient approach to classify a set of corn kernels in contact, which may contain good, or defective kernels along with impurities. The proposed approach consists of two stages, the first one is a next-generation segmentatio
Externí odkaz:
https://doaj.org/article/fa887a87dc4d4e7fa241997d189280fb
Publikováno v:
2022 16th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e826bbb87624a016ca53911fe7002933
https://doi.org/10.1109/sitis57111.2022.00037
https://doi.org/10.1109/sitis57111.2022.00037
Autor:
Henry O. Velesaca, Patricia L. Suárez, Angel D. Sappa, Dario Carpio, Rafael E. Rivadeneira, Angel Sanchez
Publikováno v:
Anais do III Workshop Brasileiro de Cidades Inteligentes (WBCI 2022).
This work compiles the different computer vision-based approaches from the state-of-the-art intended for video analytics in urban environments. The manuscript groups the different approaches according to the typical modules present in video analysis,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8fe2780cf52e337ff46a73eb39af9d76
https://doi.org/10.5753/wbci.2022.223096
https://doi.org/10.5753/wbci.2022.223096
Publikováno v:
Intelligent Systems Reference Library ISBN: 9783031063060
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b03cda53499c153fd972e65f659324e9
https://doi.org/10.1007/978-3-031-06307-7_5
https://doi.org/10.1007/978-3-031-06307-7_5
Autor:
Henry O. Velesaca, Patricia L. Suárez, Dario Carpio, Rafael E. Rivadeneira, Ángel Sánchez, Angel D. Sappa
Publikováno v:
Intelligent Systems Reference Library ISBN: 9783031063060
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::736fa16898ba2ffa47e8e859a3b25023
https://doi.org/10.1007/978-3-031-06307-7_6
https://doi.org/10.1007/978-3-031-06307-7_6
Publikováno v:
Advances in Visual Computing ISBN: 9783030904388
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5c9b3b4b9b587aae3bf4e10a1a1a620f
https://doi.org/10.1007/978-3-030-90439-5_11
https://doi.org/10.1007/978-3-030-90439-5_11
Publikováno v:
IWSSIP
This paper presents the design and implementation details of a system build-up by using off-the-shelf algorithms for urban video analytics. The system allows the connection to public video surveillance camera networks to obtain the necessary informat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f4624f83f6b84b03e1f50cb9401a25e9
https://doi.org/10.1109/iwssip48289.2020.9145121
https://doi.org/10.1109/iwssip48289.2020.9145121
Publikováno v:
CVPR Workshops
This paper presents a full pipeline to classify sample sets of corn kernels. The proposed approach follows a segmentation-classification scheme. The image segmentation is performed through a well known deep learning-based approach, the Mask R-CNN arc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d3621110d56159dcfda4986da79a224f
https://doi.org/10.1109/cvprw50498.2020.00041
https://doi.org/10.1109/cvprw50498.2020.00041
Publikováno v:
VISIGRAPP (4: VISAPP)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0a203e45ef23ae41e1493bb83d81d871
https://doi.org/10.5220/0009167604980505
https://doi.org/10.5220/0009167604980505
Publikováno v:
Image and Vision Computing. 110:104182
This paper presents a domain adaptation strategy to efficiently train network architectures for estimating the relative camera pose in multi-view scenarios. The network architectures are fed by a pair of simultaneously acquired images, hence in order
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5666375437455e2ec5edff5946d00bd0
https://doi.org/10.1016/j.imavis.2021.104182
https://doi.org/10.1016/j.imavis.2021.104182