Výsledky vyhledávání - "Jose David Bermudez Castro"

Akademický článek

Weakly Supervised Domain Adversarial Neural Network for Deforestation Detection in Tropical Forests

Autor: Pedro Juan Soto Vega, Gilson Alexandre Ostwald Pedro da Costa, Mabel Ximena Ortega Adarme, Jose David Bermudez Castro, Raul Queiroz Feitosa

Publikováno v: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 10264-10278 (2023)

Domain adaptation has proven to be suitable for alleviating domain discrepancies, which hinder the generalization capacity of classifiers. Among a few alternatives, domain adaptation techniques that align features in a domain-agnostic space through a

Externí odkaz: https://doaj.org/article/e5d40fc1c31047f5bd2810759ee1d29a

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[pt] SINTETIZAÇÃO DE IMAGENS ÓTICAS MULTIESPECTRAIS A PARTIR DE DADOS SAR/ÓTICOS USANDO REDES GENERATIVAS ADVERSARIAS CONDICIONAIS

Autor: JOSE DAVID BERMUDEZ CASTRO

[pt] Imagens óticas são frequentemente afetadas pela presença de nuvens. Com o objetivo de reduzir esses efeitos, diferentes técnicas de reconstrução foram propostas nos últimos anos. Uma alternativa comum é explorar dados de sensores ativos,

Externí odkaz: https://www.maxwell.vrac.puc-rio.br/colecao.php?strSecao=resultado&nrSeq=52114@1
https://www.maxwell.vrac.puc-rio.br/colecao.php?strSecao=resultado&nrSeq=52114@2

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[en] AGE ESTIMATION FROM FACIALS IMAGES

Autor: JOSE DAVID BERMUDEZ CASTRO

[pt] Esta dissertação tem por objetivo investigar métodos de estimação da idade a partir de imagens faciais. Avalia-se o impacto de distintos fatores sobre a acurácia da estimativa, especificamente, a acurácia da localização de pontos fiduci

Externí odkaz: https://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=25755@1
https://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=25755@2

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Dissertation/ Thesis

AGE ESTIMATION FROM FACIALS IMAGES

Autor: JOSE DAVID BERMUDEZ CASTRO

Publikováno v: Repositório Institucional da PUC_RIOPontifícia Universidade Católica do Rio de JaneiroPUC_RIO.

PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Esta dissertação tem por objetivo investigar métodos de estimação da idade a par

Externí odkaz: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=25755@1
http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=25755@2

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Convolutional Neural Networks Regression Model with Uncertainty Estimates to predict GEDI Canopy height at 30m resolution using multisource SAR and optical observations

Autor: Jose David Bermudez Castro, Shuhong Qin, Camile Sothe, Alemu Gonsamo

Accurate estimates of forest aboveground biomass (AGB) are essential for assessing forest carbon stocks and their change over time to support policies for climate change mitigation, resource management, and biodiversity conservation. Among the method

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::4e7dba101d2b1a2e632b5808aa8353b1
https://doi.org/10.5194/egusphere-egu23-10091

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Semi and Self-supervised Learning for Multi-label Classification for an Underwater Inspection Imagery Application

Autor: Amanda Lucas Pereira, Manoela Kohler, Jose David Bermudez Castro, Marco Aurélio Cavalcanti Pacheco

Underwater inspections are crucial for the preventive maintenance of offshore equipment from the oil and gas industry. However, the entire inspection process is costly, subjective and time-consuming. Traditionally, specialists assess equipment condit

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8dc3038321867f9c4466ab7927bda077
https://doi.org/10.21203/rs.3.rs-2899695/v1

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Atrous cGAN for SAR to Optical Image Translation

Autor: Jose David Bermudez Castro, Pedro Juan Soto Vega, Daliana Lobo Torres, Raul Queiroz Feitosa, P. N. Happ, Javier Noa Turnes

Publikováno v: IEEE Geoscience and Remote Sensing Letters. 19:1-5

Conditional (cGAN)-based methods proposed so far for synthetic aperture radar (SAR)-to-optical image synthesis tend to produce noisy and unsharp optical outcomes. In this work, we propose the atrous-cGAN, a novel cGAN architecture that improves the S

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::bdd52723b72c2fafef97adec50663f7b
https://doi.org/10.1109/lgrs.2020.3031199

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EVALUATING A CONVOLUTIONAL NEURAL NETWORK FOR FEATURE EXTRACTION AND TREE SPECIES CLASSIFICATION USING UAV-HYPERSPECTRAL IMAGES

Autor: Carla Luciane Lima, Laura Elena Cué La Rosa, C. M. de Almeida, Antonio Maria Garcia Tommaselli, Marcos Benedito Schimalski, Gabriela Takahashi Miyoshi, Camile Sothe, Jose David Bermudez Castro, Michele Dalponte, Alemu Gonsamo, Raul Queiroz Feitosa, Veraldo Liesenberg

Publikováno v: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-3-2020, Pp 193-199 (2020)

The classification of tree species can significantly benefit from high spatial and spectral information acquired by unmanned aerial vehicles (UAVs) associated with advanced feature extraction and classification methods. Different from the traditional

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53388a0b327e0fe93245591c13a7d888
https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2020/193/2020/isprs-annals-V-3-2020-193-2020.pdf

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Comparative performance of convolutional neural network, weighted and conventional support vector machine and random forest for classifying tree species using hyperspectral and photogrammetric data

Autor: C. M. de Almeida, Jose David Bermudez Castro, Laura Elena Cué La Rosa, Carla Luciane Lima, Antonio Maria Garcia Tommaselli, Camile Sothe, Michele Dalponte, Raul Queiroz Feitosa, Marcos Benedito Schimalski, Gabriela Takahashi Miyoshi, Veraldo Liesenberg

Publikováno v: Scopus
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP

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Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2e975ac907abdcedf934584dd64f6e45
https://doi.org/10.1080/15481603.2020.1712102

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