Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Haeyun Lee"'
Autor:
Kyungsu Lee, Haeyun Lee, Moon Hwan Lee, Jin Ho Chang, C.-C. Jay Kuo, Seung-June Oh, Jonghye Woo, Jae Youn Hwang
Publikováno v:
APSIPA Transactions on Signal and Information Processing, Vol 12, Iss 5 (2024)
Externí odkaz:
https://doaj.org/article/c879397bebf9491c9b10b78f42565afd
Autor:
Haeyun Lee, Kyungsu Lee, Jun Hee Kim, Younghwan Na, Juhum Park, Jihwan P. Choi, Jae Youn Hwang
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 4139-4149 (2021)
Change detection is an important task in the field of remote sensing. Various change detection methods based on convolutional neural networks (CNNs) have recently been proposed for remote sensing using satellite or aerial images. However, existing me
Externí odkaz:
https://doaj.org/article/61f400ece15d4b15ae407e6ae1b6e95f
Autor:
Haeyun Lee, Sunghyun Cho
Publikováno v:
IEEE Access, Vol 8, Pp 34686-34695 (2020)
Channel attention has recently been proposed and shown a great improvement in image classification accuracy. In this paper, we show that channel attention can greatly help a low-level vision task, image denoising, as well, and propose channel attenti
Externí odkaz:
https://doaj.org/article/4166b0701f874765a09c3b6f73e3c69e
Autor:
Dayoung Jeong, Hanul Son, Yunhui Lim, Chaewon Moon, Haeyun Lee, Youkyoung Oh, Minchae Kim, Jui Kim
Publikováno v:
Journal of the Korea Academia-Industrial cooperation Society. 23:474-484
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::375effae1e51e835dfcb4e590bf7c448
https://doi.org/10.5762/kais.2022.23.12.474
https://doi.org/10.5762/kais.2022.23.12.474
Autor:
Jeong Hyun Ha, Haeyun Lee, Seok Min Kwon, Hyunjin Joo, Guang Lin, Deok-Yeol Kim, Sukwha Kim, Jae Youn Hwang, Jee-Hyeok Chung, Hyoun-Joong Kong
Publikováno v:
Journal of Craniofacial Surgery; Nov/Dec2023, Vol. 34 Issue 8, p2369-2375, 7p
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-17
Various deep learning-based segmentation models have been developed to segment buildings in aerial images. However, the segmentation maps predicted by the conventional convolutional neural network-based methods cannot accurately determine the shapes
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::28f876ae931644de77d9e5c903469519
https://doi.org/10.1109/tgrs.2021.3089623
https://doi.org/10.1109/tgrs.2021.3089623
Publikováno v:
IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 69(9)
The performance of computer-aided diagnosis (CAD) systems that are based on ultrasound imaging has been enhanced owing to the advancement in deep learning. However, because of the inherent speckle noise in ultrasound images, the ambiguous boundaries
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fabfc58bdd46c32d22789d6d4c19a33e
https://pubmed.ncbi.nlm.nih.gov/35877808
https://pubmed.ncbi.nlm.nih.gov/35877808
Autor:
Kyungsu Lee, Jihwan P. Choi, Jun Hee Kim, Haeyun Lee, Juhum Park, Younghwan Na, Jae Youn Hwang
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 4139-4149 (2021)
Change detection is an important task in the field of remote sensing. Various change detection methods based on convolutional neural networks (CNNs) have recently been proposed for remote sensing using satellite or aerial images. However, existing me
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c3b4e3ad6dd4986bf74c3bae50342baa
https://ieeexplore.ieee.org/document/9387529/
https://ieeexplore.ieee.org/document/9387529/
Background and Objective: Incomplete Kawasaki disease (KD) has often been misdiagnosed due to a lack of the clinical manifestations of classic KD. However, it is associated with a markedly higher prevalence of coronary artery lesions. Identifying cor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b17e493b290b2a4b89e38c875fd0bd2
http://arxiv.org/abs/2204.02403
http://arxiv.org/abs/2204.02403
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision (ICCV).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d7d47833e4b78a11614324376512b6c7
https://doi.org/10.1109/iccv48922.2021.00698
https://doi.org/10.1109/iccv48922.2021.00698