Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Yi-Mei Kuo"'
Autor:
Jamie Podell, Shiming Yang, Serenity Miller, Ryan Felix, Hemantkumar Tripathi, Gunjan Parikh, Catriona Miller, Hegang Chen, Yi-Mei Kuo, Chien Yu Lin, Peter Hu, Neeraj Badjatia
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)
Abstract Secondary neurologic decline (ND) after traumatic brain injury (TBI) is independently associated with outcome, but robust predictors of ND are lacking. In this retrospective analysis of consecutive isolated TBI admissions to the R. Adams Cow
Externí odkaz:
https://doaj.org/article/b031e0a3eac04b3382d7d56c5bf02934
Publikováno v:
Remote Sensing, Vol 16, Iss 6, p 942 (2024)
Band clustering has been widely used for hyperspectral band selection (BS). However, selecting an appropriate band to represent a band cluster is a key issue. Density peak clustering (DPC) provides an effective means for this purpose, referred to as
Externí odkaz:
https://doaj.org/article/b8d30c7c2155471989d8f987a74b7281
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 3986-4007 (2020)
Hyperspectral image classification (HSIC) has generated considerable interests over the past years. However, one of challenging issues arising in HSIC is inconsistent classification, which is mainly caused by random training sampling (RTS) of selecti
Externí odkaz:
https://doaj.org/article/d207e658693747a3822fcb5915c71b27
Autor:
Yi-Mei Kuo, 郭議鎂
105
In the recent years, agricultural product quality inspection has been getting more and more respected. The traditional quality inspection methods usually need large manual operations or destructive processing to subjects. As the technique of
In the recent years, agricultural product quality inspection has been getting more and more respected. The traditional quality inspection methods usually need large manual operations or destructive processing to subjects. As the technique of
Externí odkaz:
http://ndltd.ncl.edu.tw/handle/65686817253410475427
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 59:5979-5997
Due to significant inter-band correlation resulting from the use of hundreds of contiguous spectral bands, band selection (BS) is commonly used to reduce data dimensionality for band redundancy removal. A challenge for BS is how to design an effectiv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::23ca79150ddbe6a253061983ea2107b2
https://doi.org/10.1109/tgrs.2020.3024602
https://doi.org/10.1109/tgrs.2020.3024602
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 3986-4007 (2020)
Hyperspectral image classification (HSIC) has generated considerable interests over the past years. However, one of challenging issues arising in HSIC is inconsistent classification, which is mainly caused by random training sampling (RTS) of selecti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::98ac432bf667533d6d08bdf293b1675c
https://ieeexplore.ieee.org/document/9137652/
https://ieeexplore.ieee.org/document/9137652/
Publikováno v:
IGARSS
Hyperspectral image classification (HSIC) has received considerable interest in recent years. In particular, spectral-spatial classification methods are proposed to jointly consider spectral and spatial together. However, one of challenging issues in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::faaa90b4736fec8e219cb1858df46aef
https://doi.org/10.1109/igarss.2019.8898826
https://doi.org/10.1109/igarss.2019.8898826
Improving pesticide residues detection using band prioritization and constrained energy minimization
Publikováno v:
IGARSS
This paper presents an emerging method to detect pesticide residues on fruit. In order to enhance pesticide signature intensity and make the detection rate of pesticide better, we applied band weighting process and band selection (BS) process base on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0f351827c9fcf072100e3f0ad75ce924
https://doi.org/10.1109/igarss.2017.8128076
https://doi.org/10.1109/igarss.2017.8128076