Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Tiep H. Vu"'
Publikováno v:
IEEE Transactions on Aerospace and Electronic Systems. 55:1712-1724
Using low-frequency (UHF to L-band) ultrawideband synthetic aperture radar (SAR) technology for detecting buried and obscured targets, e.g., bomb or mine, has been successfully demonstrated recently. Despite promising recent progress, a significant o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b684e58b6a10e4a21c0f6866932c18df
https://doi.org/10.1109/taes.2018.2875504
https://doi.org/10.1109/taes.2018.2875504
Autor:
Vishal Monga, William Deaderick, Harrison X. Bai, Chang Su, Arvind Rao, Srikanth Kuthuru, Tiep H. Vu
Publikováno v:
Cancer Informatics, Vol 17 (2018)
Cancer Informatics
Cancer Informatics
Radiomics is a rapidly growing field in which sophisticated imaging features are extracted from radiology images to predict clinical outcomes/responses, genetic alterations, and other outcomes relevant to a patient’s prognosis or response to therap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::05b607003fbafed503b7941759ed496b
https://doaj.org/article/f998f35a2cbb411ca9c1feb5e5b116b6
https://doaj.org/article/f998f35a2cbb411ca9c1feb5e5b116b6
Publikováno v:
2018 IEEE Radar Conference (RadarConf18).
Classifying buried and obscured targets of interest from other natural and manmade clutter objects in the scene is an important problem for the U.S. Army. Targets of interest are often represented by signals captured using low-frequency (UHF to L-ban
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bcee94746ac2c4e9f586ffab14f3a728
https://doi.org/10.1109/radar.2018.8378619
https://doi.org/10.1109/radar.2018.8378619
Autor:
Ch V. Sai Praveen, Dacheng Tao, Deqing Sun, Jae-Seok Choi, Giang Bui, Luc Van Gool, Lei Zhang, Qi Guo, Yunjin Chen, Karen Egiazarian, Xintao Wang, Ke Yu, Jiahui Yu, Bee Oh Lim, Hojjat Seyed Mousavi, Seungjun Nah, Yulun Zhang, Ming-Hsuan Yang, Yapeng Tian, Tiep H. Vu, Zhimin Tang, Heewon Kim, Cristóvão Cruz, Vishal Monga, Yuchen Fan, Chen Change Loy, Rakesh Mehta, Jinshan Pan, Yoseob Han, Ye Duan, Truc Le, Yu Qiao, Ruxin Wang, Xiangyu Xu, Xuan-Phung Huynh, Chao Dong, Xu Jinchang, Jaejun Yoo, Thomas S. Huang, Radu Timofte, Wei Han, Xueying Qin, Zhiqiang Xia, Shaohui Li, Xu Lin, Haichao Yu, Yujin Zhang, Vladimir Katkovnik, Honghui Shi, Yu Zhao, Woong Bae, Zhengtao Wang, Abhinav Agarwalla, Arnav Kumar Jain, Ding Liu, Liang Lin, Xibin Song, Che Zhu, Wangmeng Zuo, Wen Heng, Xinchao Wang, Shixiang Wu, Zhangyang Wang, Sanghyun Son, Hongdiao Wen, Jianxin Pang, Kyoung Mu Lee, Linkai Luo, Eirikur Agustsson, Ruofan Zhou, Yuchao Dai, Min Fu, Tiantong Guo, Munchurl Kim, Jong Chul Ye, Lei Cao, Kai Zhang
Publikováno v:
CVPR Workshops
This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results. A new DIVerse 2K resolution image dataset (DIV2K) was employed. The challen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8c975a4416e3af70966bca366ab5260f
https://doi.org/10.1109/cvprw.2017.149
https://doi.org/10.1109/cvprw.2017.149
Publikováno v:
CVPR Workshops
Recent advances have seen a surge of deep learning approaches for image super-resolution. Invariably, a network, e.g. a deep convolutional neural network (CNN) or auto-encoder is trained to learn the relationship between low and high-resolution image
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5fd23565b1aa6c61572f51b3af9571fe
https://doi.org/10.1109/cvprw.2017.148
https://doi.org/10.1109/cvprw.2017.148
Publikováno v:
2017 IEEE Radar Conference (RadarConf).
Although a lot of progress has been made over the years, one critical challenge still facing low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar (SAR) technology is the discrimination of buried and obscured targets of interest
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eff955db86094e60a208b93a24850d7b
https://doi.org/10.1109/radar.2017.7944265
https://doi.org/10.1109/radar.2017.7944265
Autor:
Vishal Monga, Tiep H. Vu
Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. This observation has been exploited partially in a recently proposed dictionary learning framework by separating the particular
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::307f70384fb9c0b7f47273f553a5d5a3
http://arxiv.org/abs/1610.08606
http://arxiv.org/abs/1610.08606
Autor:
Tiep H. Vu, Vishal Monga
Publikováno v:
ICIP
Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. Inspired by this observation, we propose a novel method to explicitly and simultaneously learn a set of common patterns as well
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::90c1d4cf17f25eb32765241dd213bd88
Publikováno v:
ICASSP
Spike and Slab priors have been of much recent interest in signal processing as a means of inducing sparsity in Bayesian inference. Applications domains that benefit from the use of these priors include sparse recovery, regression and classification.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f29844b7c2fc2084a829f061008bcf1f
DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification
In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structure. In this paper, we propose an auto
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::510a8d8b2a4de6678bb08391c2ae8545
http://arxiv.org/abs/1502.01032
http://arxiv.org/abs/1502.01032