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of 697
pro vyhledávání: '"CHEN Weihai"'
Due to saturated regions of inputting low dynamic range (LDR) images and large intensity changes among the LDR images caused by different exposures, it is challenging to produce an information enriched panoramic LDR image without visual artifacts for
Externí odkaz:
http://arxiv.org/abs/2409.04679
Autor:
ZHOU Yun, CHEN Weihai, LIU Yongbiao, YAN Nan, SUN Xiangshang, LIAO Wentao, LIU Junya, PU Yuehu
Publikováno v:
Fushe yanjiu yu fushe gongyi xuebao, Vol 41, Iss 3, Pp 030304-030304 (2023)
In this paper, a new collimator based on Hygeia GMX-1 cobalt (60Co) radiosurgery system was designed and analyzed using the Monte Carlo simulation program GEANT4. The results of EBT3 film measurements showed that the field penumbra of the improved co
Externí odkaz:
https://doaj.org/article/e68ce745006b473a9fd649b01ea00f72
Publikováno v:
Open Medicine, Vol 14, Iss 1, Pp 224-233 (2019)
The aim of the present study was to verify the effectiveness of physiological ischemic training (PIT) in patients with coronary heart disease (CHD) and compare differences in clinical outcomes between isometric exercise training (IET) and cuff inflat
Externí odkaz:
https://doaj.org/article/3051f50178e74a47a731700dd2f39e48
The distribution shift of electroencephalography (EEG) data causes poor generalization of braincomputer interfaces (BCIs) in unseen domains. Some methods try to tackle this challenge by collecting a portion of user data for calibration. However, it i
Externí odkaz:
http://arxiv.org/abs/2405.11163
Over the past few years, self-supervised monocular depth estimation that does not depend on ground-truth during the training phase has received widespread attention. Most efforts focus on designing different types of network architectures and loss fu
Externí odkaz:
http://arxiv.org/abs/2311.07198
Over the past few years, monocular depth estimation and completion have been paid more and more attention from the computer vision community because of their widespread applications. In this paper, we introduce novel physics (geometry)-driven deep le
Externí odkaz:
http://arxiv.org/abs/2311.07166
Monocular depth estimation (MDE) is a fundamental topic of geometric computer vision and a core technique for many downstream applications. Recently, several methods reframe the MDE as a classification-regression problem where a linear combination of
Externí odkaz:
http://arxiv.org/abs/2309.14137
Monocular depth estimation has drawn widespread attention from the vision community due to its broad applications. In this paper, we propose a novel physics (geometry)-driven deep learning framework for monocular depth estimation by assuming that 3D
Externí odkaz:
http://arxiv.org/abs/2309.10592
Online unsupervised video object segmentation (UVOS) uses the previous frames as its input to automatically separate the primary object(s) from a streaming video without using any further manual annotation. A major challenge is that the model has no
Externí odkaz:
http://arxiv.org/abs/2306.12048
Image keypoints and descriptors play a crucial role in many visual measurement tasks. In recent years, deep neural networks have been widely used to improve the performance of keypoint and descriptor extraction. However, the conventional convolution
Externí odkaz:
http://arxiv.org/abs/2304.03608