Zobrazeno 1 - 10
of 137
pro vyhledávání: '"Jefersson A. Dos Santos"'
Publikováno v:
IEEE Access, Vol 11, Pp 12547-12554 (2023)
In some scenarios, a single input image may not be enough to allow the object classification. In those cases, it is crucial to explore the complementary information extracted from images presenting the same object from multiple perspectives (or views
Externí odkaz:
https://doaj.org/article/9cbd417a399e492eaed8526bdb6617dc
Publikováno v:
IEEE Access, Vol 9, Pp 114308-114324 (2021)
Over the past decade, Convolutional Networks (ConvNets) have renewed the perspectives of the research and industrial communities. Although this deep learning technique may be composed of multiple layers, its core operation is the convolution, an impo
Externí odkaz:
https://doaj.org/article/03bdbe6ad6cf495a9a3d116967723814
Publikováno v:
IEEE Access, Vol 8, Pp 84037-84062 (2020)
Digitization techniques for biomedical images yield disparate visual patterns in radiological exams. These pattern differences, which can be viewed as a domain-shift problem, may hamper the use of data-driven approaches for inference over these image
Externí odkaz:
https://doaj.org/article/bcb755d509e74ceca7904f38d95b78e9
Autor:
Juan F. H. Albarracín, Rafael S. Oliveira, Marina Hirota, Jefersson A. dos Santos, Ricardo da S. Torres
Publikováno v:
Remote Sensing, Vol 12, Iss 14, p 2267 (2020)
We introduce a soft computing approach for automatically selecting and combining indices from remote sensing multispectral images that can be used for classification tasks. The proposed approach is based on a Genetic-Programming (GP) framework, a tec
Externí odkaz:
https://doaj.org/article/91315ecc3c4b42399f2bf6c2d3215068
Autor:
Keiller Nogueira, Gabriel L. S. Machado, Pedro H. T. Gama, Caio C. V. da Silva, Remis Balaniuk, Jefersson A. dos Santos
Publikováno v:
Remote Sensing, Vol 12, Iss 4, p 739 (2020)
Soil erosion is considered one of the most expensive natural hazards with a high impact on several infrastructure assets. Among them, railway lines are one of the most likely constructions for the appearance of erosion and, consequently, one of the m
Externí odkaz:
https://doaj.org/article/388a6821eff14a398d6106b83ce2dab5
Publikováno v:
Computers & Graphics. 113:77-88
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb30cc706267787e8048354ed9d52d86
https://doi.org/10.1016/j.cag.2023.05.009
https://doi.org/10.1016/j.cag.2023.05.009
Autor:
Thiago Carvalho, Jorge A. Chamorro Martinez, Hugo Oliveira, Jefersson A. dos Santos, Raul Queiroz Feitosa
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 20:1-5
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::650166219f82dabc05912979463381a4
https://doi.org/10.1109/lgrs.2023.3244532
https://doi.org/10.1109/lgrs.2023.3244532
Publikováno v:
ACM Transactions on Intelligent Systems and Technology. 14:1-25
We propose a novel deep metric learning method. Differently from many works in this area, we define a novel latent space obtained through an autoencoder. The new space, namely S-space, is divided into different regions describing positions where pair
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cba27107751c877abcc5a32d49baf1ba
https://doi.org/10.1145/3559766
https://doi.org/10.1145/3559766
Autor:
Veraldo Liesenberg, Nayara Vasconcelos Estrabis, Jose Marcato Junior, Alessandra Rodrigues Gomes, Rafael Lemos Paes, Rosa Elvira Correa Pabon, Jefersson Alex Dos Santos
Publikováno v:
IEEE Geoscience and Remote Sensing Magazine. 10:354-360
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::66fd5d012c18ce931c8e5a0ccd5409c1
https://doi.org/10.1109/mgrs.2022.3147422
https://doi.org/10.1109/mgrs.2022.3147422
Autor:
Jefersson A. dos Santos, Jorge Andres Chamorro Martinez, Hugo N. Oliveira, Raul Queiroz Feitosa
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 19:1-5
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8cdcbb89558c9d8e33d4f4fecd2ba0c6
https://doi.org/10.1109/lgrs.2021.3113828
https://doi.org/10.1109/lgrs.2021.3113828