Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Dong Huh"'
Publikováno v:
Remote Sensing, Vol 12, Iss 20, p 3461 (2020)
We propose an unsupervised network with adversarial learning, the Raindrop-aware GAN, which enhances the quality of coastal video images contaminated by raindrops. Raindrop removal from coastal videos faces two main difficulties: converting the degra
Externí odkaz:
https://doaj.org/article/be5395cfe78541cbb5a7d22dec691f37
Publikováno v:
Atmosphere, Vol 11, Iss 3, p 304 (2020)
In this paper, we propose a series of procedures for coastal wave-tracking using coastal video imagery with deep neural networks. It consists of three stages: video enhancement, hydrodynamic scene separation and wave-tracking. First, a generative adv
Externí odkaz:
https://doaj.org/article/8dceb40f28494202b95f1243cc73a6c5
Publikováno v:
Journal of the Korea Computer Graphics Society. 25:1-9
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::64259a5ca485bc621d9cebb612dd6fe4
https://doi.org/10.15701/kcgs.2019.25.5.1
https://doi.org/10.15701/kcgs.2019.25.5.1
Publikováno v:
Atmosphere, Vol 11, Iss 3, p 304 (2020)
Atmosphere
Volume 11
Issue 3
Atmosphere
Volume 11
Issue 3
In this paper, we propose a series of procedures for coastal wave-tracking using coastal video imagery with deep neural networks. It consists of three stages: video enhancement, hydrodynamic scene separation and wave-tracking. First, a generative adv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::89802bb4b516b03beb49ae7a88a89e9c
https://www.mdpi.com/2073-4433/11/3/304
https://www.mdpi.com/2073-4433/11/3/304
Publikováno v:
ICAIIC
Object localization in video is to predict the location and image boundaries of objects of interest in sequential scenes. Despite numerous methods being developed for the task, there are still challenging issues, such as labor-intensive data preparat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cb36f28085c766ebf2090a29c6c31391
https://doi.org/10.1109/icaiic.2019.8668987
https://doi.org/10.1109/icaiic.2019.8668987
Publikováno v:
Remote Sensing, Vol 12, Iss 3461, p 3461 (2020)
Remote Sensing
Volume 12
Issue 20
Pages: 3461
Remote Sensing
Volume 12
Issue 20
Pages: 3461
We propose an unsupervised network with adversarial learning, the Raindrop-aware GAN, which enhances the quality of coastal video images contaminated by raindrops. Raindrop removal from coastal videos faces two main difficulties: converting the degra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cb5168cf2dd2e469d961fc91927b13e5
https://doi.org/10.3390/rs12203461
https://doi.org/10.3390/rs12203461
Autor:
Man Dong Huh, Minjeong Lyu
Publikováno v:
EDUCATIONAL RESEARCH. 54:165-185
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f3cbfd7374b707301c564be66d3b60f2
https://doi.org/10.17253/swueri.2012.54..007
https://doi.org/10.17253/swueri.2012.54..007
Autor:
Man Dong Huh, Shin， Jae-Suk
Publikováno v:
The Journal of Korean Dance. 29:305-325
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d233cca35e364bbf0e71a0dbcd249012
https://doi.org/10.15726/jkd.2011.29.3.012
https://doi.org/10.15726/jkd.2011.29.3.012
Autor:
Chang-Hoon Bang, Man-Dong Huh
Publikováno v:
Journal of Korean Society of Hazard Mitigation. 11:45-49
The aim of study intends to investigate effect of weight of fire-protective clothing for physical balance and agility after maximum physical activity and to provide the base data for the safety of firefighter. For evaluation of static and dynamic-bal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8dd9817b7978708882984a5f8efc4a8a
https://doi.org/10.9798/kosham.2011.11.1.045
https://doi.org/10.9798/kosham.2011.11.1.045
Publikováno v:
Journal of Sport and Leisure Studies. 37:1091-1099
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::abd8782a233d2d082ec730cc6e0fbcce
https://doi.org/10.51979/kssls.2009.08.37.1091
https://doi.org/10.51979/kssls.2009.08.37.1091