Zobrazeno 1 - 10
of 318
pro vyhledávání: '"Tran, Trac. D."'
Autor:
Remedios, Samuel W., Han, Shuo, Xue, Yuan, Carass, Aaron, Tran, Trac D., Pham, Dzung L., Prince, Jerry L.
In 2D multi-slice magnetic resonance (MR) acquisition, the through-plane signals are typically of lower resolution than the in-plane signals. While contemporary super-resolution (SR) methods aim to recover the underlying high-resolution volume, the e
Externí odkaz:
http://arxiv.org/abs/2209.02611
Publikováno v:
IEEE Transactions on Signal Processing, Vol. 70, pp. 2062-2077, Apr. 2022
Designing efficient sparse recovery algorithms that could handle noisy quantized measurements is important in a variety of applications -- from radar to source localization, spectrum sensing and wireless networking. We take advantage of the approxima
Externí odkaz:
http://arxiv.org/abs/2205.10448
Optical flow estimation with occlusion or large displacement is a problematic challenge due to the lost of corresponding pixels between consecutive frames. In this paper, we discover that the lost information is related to a large quantity of motion
Externí odkaz:
http://arxiv.org/abs/2101.06333
In 2D+3D facial expression recognition (FER), existing methods generate multi-view geometry maps to enhance the depth feature representation. However, this may introduce false estimations due to local plane fitting from incomplete point clouds. In th
Externí odkaz:
http://arxiv.org/abs/2011.08333
This paper addresses the challenging unsupervised scene flow estimation problem by jointly learning four low-level vision sub-tasks: optical flow $\textbf{F}$, stereo-depth $\textbf{D}$, camera pose $\textbf{P}$ and motion segmentation $\textbf{S}$.
Externí odkaz:
http://arxiv.org/abs/2011.08332
In order to reduce hardware complexity and power consumption, massive multiple-input multiple-output (MIMO) systems employ low-resolution analog-to-digital converters (ADCs) to acquire quantized measurements $\boldsymbol y$. This poses new challenges
Externí odkaz:
http://arxiv.org/abs/2007.14564
Autor:
Huang, Shuai, Tran, Trac D.
1-bit compressive sensing aims to recover sparse signals from quantized 1-bit measurements. Designing efficient approaches that could handle noisy 1-bit measurements is important in a variety of applications. In this paper we use the approximate mess
Externí odkaz:
http://arxiv.org/abs/2007.07679
Autor:
Rangamani, Akshay, Nguyen, Nam H., Kumar, Abhishek, Phan, Dzung, Chin, Sang H., Tran, Trac D.
It has been empirically observed that the flatness of minima obtained from training deep networks seems to correlate with better generalization. However, for deep networks with positively homogeneous activations, most measures of sharpness/flatness a
Externí odkaz:
http://arxiv.org/abs/1902.02434
Bagging, a powerful ensemble method from machine learning, improves the performance of unstable predictors. Although the power of Bagging has been shown mostly in classification problems, we demonstrate the success of employing Bagging in sparse regr
Externí odkaz:
http://arxiv.org/abs/1812.08808
Ultra-wideband (UWB) radar systems nowadays typical operate in the low frequency spectrum to achieve penetration capability. However, this spectrum is also shared by many others communication systems, which causes missing information in the frequency
Externí odkaz:
http://arxiv.org/abs/1812.04744