Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Edemir Ferreira"'
Autor:
Gabriel Machado, Edemir Ferreira, Keiller Nogueira, Hugo Oliveira, Matheus Brito, Pedro Henrique Targino Gama, Jefersson Alex dos Santos
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 488-503 (2021)
It is undeniable that aerial/satellite images can provide useful information for a large variety of tasks. But, since these images are always taken from above, some applications can benefit from complementary information provided by other perspective
Externí odkaz:
https://doaj.org/article/758286b95b1e42f5bbe1dd012e4111a0
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:
Jefersson A. dos Santos, Edemir Ferreira, Gabriel L. S. Machado, Keiller Nogueira, Matheus Brito, Pedro H. T. Gama, Hugo N. Oliveira
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 488-503 (2021)
It is undeniable that aerial/satellite images can provide useful information for a large variety of tasks. But, since these images are always taken from above, some applications can benefit from complementary information provided by other perspective
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3e4857a8a45541628d2fbacb45231fc5
https://ieeexplore.ieee.org/document/9238485/
https://ieeexplore.ieee.org/document/9238485/
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-3-W12-2020, Pp 343-348 (2020)
In this work we present BrazilDAM, a novel public dataset based on Sentinel-2 and Landsat-8 satellite images covering all tailings dams cataloged by the Brazilian National Mining Agency (ANM). The dataset was built using georeferenced images from 769
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f93fbd805561c0d60d6e9abc9b019a31
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W12-2020/343/2020/isprs-archives-XLII-3-W12-2020-343-2020.pdf
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W12-2020/343/2020/isprs-archives-XLII-3-W12-2020-343-2020.pdf
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://explore.openaire.eu/search/publication?articleId=doi_dedup___::c4d163f891e2a923add4cdccf37ca0dc
https://ieeexplore.ieee.org/document/9084116/
https://ieeexplore.ieee.org/document/9084116/
Publikováno v:
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications ISBN: 9783030134686
CIARP
CIARP
In this work, we investigate the application of existing unsupervised domain adaptation (UDA) approaches to the task of transferring knowledge between crop regions having different coffee patterns. Given a geographical region with fully mapped coffee
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::98f9a9b2b75225a403adb7e7dd4d8ce0
https://doi.org/10.1007/978-3-030-13469-3_9
https://doi.org/10.1007/978-3-030-13469-3_9
Autor:
Edemir Ferreira, Ricardo da Silva Torres, Juan Felipe Hernandez Albarracin, Jefersson A. dos Santos
Publikováno v:
IGARSS
This paper introduces a two-step hyper- and multi-spectral image classification approach. The first step relies on the use of a genetic programming (GP) framework to both select and combine appropriate bands. The second step is concerned with the ima
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e6a775ac61f8bbd00492dd2f94b30b1c
https://doi.org/10.1109/igarss.2017.8127013
https://doi.org/10.1109/igarss.2017.8127013
Publikováno v:
SIBGRAPI
Remote Sensing Images (RSI) have been used as a major source of data, particularly with respect to the creation of thematic maps. This process is usually modeled as a supervised learning task where the system needs to learn the patterns of interest p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6bc05c44bc29d04e6f74b572efbd5744
https://doi.org/10.1109/sibgrapi.2016.064
https://doi.org/10.1109/sibgrapi.2016.064
Publikováno v:
Repositório Institucional da UFMG
Universidade Federal de Minas Gerais (UFMG)
instacron:UFMG
Universidade Federal de Minas Gerais (UFMG)
instacron:UFMG
Sistemas de Informação Geográfica (SIGs) são ferramentas de computador que analisam, armazenam, manipulam e visualizam informações geográficas em mapas. Permitindo às pessoas mais facilmente verem, interpretarem e entenderem os padrões e rel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3056::4be8b3b244d33051c5bf0cbbc0aea71e
Autor:
William Robson Schwartz, Gabriel Resende Gonçalves, Jeferson Dos Santos, Keiller Nogueira, Carlos Caetano, Victor Hugo Melo, Edemir Ferreira Júnior, Marco Túlio Alves N. Rodrigues, Antonio Nazaré Júnior, Arthur Correia
Publikováno v:
Anais do Brazilian e-Science Workshop (BreSci).
With the advances on science, a powerful computational infrastructure is desirable to increase the performance of experiments. The same holds true for research on the Computer Vision field, which deals with large amounts of data and also requires int
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::13b0ef14c78d4de1ecc1721b63534daa
https://doi.org/10.5753/bresci.2015.7203
https://doi.org/10.5753/bresci.2015.7203