Zobrazeno 1 - 10
of 101
pro vyhledávání: '"Junseok Kwon"'
Autor:
Youjin Kim, Junseok Kwon
Publikováno v:
BMC Bioinformatics, Vol 24, Iss 1, Pp 1-16 (2023)
Abstract Background Protein secondary structures that link simple 1D sequences to complex 3D structures can be used as good features for describing the local properties of protein, but also can serve as key features for predicting the complex 3D stru
Externí odkaz:
https://doaj.org/article/9bd705bb6ce94557a1bb7aa2748e122a
Publikováno v:
IEEE Access, Vol 11, Pp 26086-26098 (2023)
In this paper, we propose a novel dual gradient-based desnowing algorithm that can accurately remove snow from a scene by characterizing snow particles. To localize snow in an image, we present a gradient-based snow activation map that can be estimat
Externí odkaz:
https://doaj.org/article/d111f16c329f444eb3cbf065938d0872
Autor:
Jiaying Liu, Wen-Huang Cheng, Jenq-Neng Hwang, lvan V. Bajic, Shiqi Wang, Junseok Kwon, Ngai-Man Cheung, Rei Kawakami
Publikováno v:
APSIPA Transactions on Signal and Information Processing, Vol 12, Iss 4 (2023)
Externí odkaz:
https://doaj.org/article/591e8cf90dd34dc2b490fe8f4abe6a08
Publikováno v:
IEEE Access, Vol 10, Pp 116141-116151 (2022)
Learning deep neural networks from noisy labels is challenging, because high-capacity networks attempt to describe data even with noisy class labels. In this study, we propose a self-augmentation method without additional parameters, which handles no
Externí odkaz:
https://doaj.org/article/fa61df39eb6f47c3af6f046b65f9490e
Publikováno v:
IEEE Access, Vol 10, Pp 9022-9035 (2022)
We formulate the visual tracking problem as a semi-supervised continual learning problem, where only an initial frame is labeled. In contrast to conventional meta-learning based approaches that regard visual tracking as an instance detection problem
Externí odkaz:
https://doaj.org/article/3063a4ad6a5040f7bfd5bb72f956a3d9
Autor:
Youjin Kim, Junseok Kwon
Publikováno v:
IEEE Access, Vol 10, Pp 33527-33536 (2022)
In this study, we propose a novel Wasserstein distributional tracking method that can balance approximation with accuracy in terms of Monte Carlo estimation. To achieve this goal, we present three different systems: sliced Wasserstein-based (SWT), pr
Externí odkaz:
https://doaj.org/article/96d0396fdd074f1a89dd10d75328a95b
Autor:
Jin Wook Paeng, Junseok Kwon
Publikováno v:
IET Signal Processing, Vol 15, Iss 6, Pp 365-374 (2021)
Abstract The authors present a novel tracking algorithm based on a factorial hidden Markov model (FHMM) that can utilise the structured information of a target. An FHMM consists of multiple hidden Markov models (HMMs), wherein each HMM aims to repres
Externí odkaz:
https://doaj.org/article/d7d2498827ae4e78a85bba1dffcec1f7
Publikováno v:
IEEE Access, Vol 9, Pp 144699-144712 (2021)
In this paper, we tackle the well-known problem of dataset construction from the point of its generation using generative adversarial networks (GAN). As semantic information of the dataset should have a proper alignment with images, controlling the i
Externí odkaz:
https://doaj.org/article/78a98bdcd9b54ed0a9f64108b16fa9c4
Autor:
Sung Woo Park, Junseok Kwon
Publikováno v:
IET Computer Vision, Vol 13, Iss 4, Pp 420-427 (2019)
In this study, the authors propose an object proposal algorithm that can accurately propose object candidate regions at each frame, despite noise in a video. Accordingly, they define three orthogonal planes, namely vertical–horizontal, temporal–v
Externí odkaz:
https://doaj.org/article/118f89f3d5a04dc39f347b2935879dd0
Autor:
Guisik Kim, Junseok Kwon
Publikováno v:
IET Computer Vision, Vol 13, Iss 1, Pp 1-7 (2019)
Here, the authors propose a novel tracking algorithm that can automatically modify the initial configuration of a target to improve the tracking accuracy in subsequent frames. To achieve this goal, the authors’ method analyses the likelihood landsc
Externí odkaz:
https://doaj.org/article/59de99b7633d45de959b89beb7b372aa