Zobrazeno 1 - 10
of 458
pro vyhledávání: '"Kyoung Mu Lee"'
Publikováno v:
Bioengineering, Vol 11, Iss 6, p 568 (2024)
Ultra-widefield (UWF) retinal imaging stands as a pivotal modality for detecting major eye diseases such as diabetic retinopathy and retinal detachment. However, UWF exhibits a well-documented limitation in terms of low resolution and artifacts in th
Externí odkaz:
https://doaj.org/article/f7f25f071cde4f868096863a65475853
Publikováno v:
IEEE Access, Vol 11, Pp 145254-145263 (2023)
The task of 3D human-body reconstruction (3DHR), which mostly utilizes parametric pose and shape representations, has witnessed significant advances in recent years. However, the application of 3DHR techniques in handling real-world in-the-wild data,
Externí odkaz:
https://doaj.org/article/11105af3148f451c910e768e6ca5f518
Publikováno v:
IEEE Access, Vol 11, Pp 21799-21810 (2023)
In this paper, we propose a novel disease detection framework based on translating a given chest X-ray (CXR) to a corresponding normal CXR image. To train a model for normal CXR translation, we synthesize a paired image dataset from existing public C
Externí odkaz:
https://doaj.org/article/22563bb94c0142b29ddb4d8ec36cad91
Publikováno v:
PLoS ONE, Vol 18, Iss 3, p e0282416 (2023)
ProblemLow-quality fundus images with complex degredation can cause costly re-examinations of patients or inaccurate clinical diagnosis.AimThis study aims to create an automatic fundus macular image enhancement framework to improve low-quality fundus
Externí odkaz:
https://doaj.org/article/e7c9f9718361493ab1fa6dcecc625f97
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
Publikováno v:
Applied Sciences, Vol 11, Iss 1, p 320 (2020)
Retinal artery–vein (AV) classification is a prerequisite for quantitative analysis of retinal vessels, which provides a biomarker for neurologic, cardiac, and systemic diseases, as well as ocular diseases. Although convolutional neural networks ha
Externí odkaz:
https://doaj.org/article/e5f8536ee7074c8495255d3005d3fd06
Autor:
Seung Yeon Shin, Soochahn Lee, Il Dong Yun, Ho Yub Jung, Yong Seok Heo, Sun Mi Kim, Kyoung Mu Lee
Publikováno v:
PLoS ONE, Vol 10, Iss 12, p e0143725 (2015)
In this paper, we present a novel cascaded classification framework for automatic detection of individual and clusters of microcalcifications (μC). Our framework comprises three classification stages: i) a random forest (RF) classifier for simple fe
Externí odkaz:
https://doaj.org/article/15868500e13d4e00b4425300f763ae24
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2002, Iss 10, Pp 1127-1134 (2002)
Estimation of the shape dissimilarity between 3D models is a very important problem in both computer vision and graphics for 3D surface reconstruction, modeling, matching, and compression. In this paper, we propose a novel method called surface rovin
Externí odkaz:
https://doaj.org/article/2d281b8454154c709db4170d1af2a356
Autor:
Kyoung Mu Lee, Dong Woo Park
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2008 (2008)
We propose a new method to optimize the completely-trained boosted cascade detector on an enforced training set. Recently, due to the accuracy and real-time characteristics of boosted cascade detectors like the Adaboost, a lot of variant algorithms h
Externí odkaz:
https://doaj.org/article/e2d0dfd9fe5f45f1b22829d1a585a0ee
Publikováno v:
EURASIP Journal on Image and Video Processing, Vol 2008 (2008)
In most content-based image retrieval systems, the color information is extensively used for its simplicity and generality. Due to its compactness in characterizing the global information, a uniform quantization of colors, or a histogram, has been th
Externí odkaz:
https://doaj.org/article/e7a8c55e7b4547b68d5d23439abc9ef0