Zobrazeno 1 - 10
of 297
pro vyhledávání: '"Li Yijin"'
Autor:
Li Yijin
Publikováno v:
Online Media and Global Communication, Vol 2, Iss 3, Pp 379-403 (2023)
Live streaming has become an increasingly popular media, which allows individuals to record and broadcast simultaneously on the internet. Streamers, individuals who conduct live streaming videos, create diverse media content on live streaming video p
Externí odkaz:
https://doaj.org/article/72f00e45a0e6436e977fee0989924382
Publikováno v:
IEEE Access, Vol 10, Pp 38502-38510 (2022)
The classification of voltage sag sources is essential for the establishment of controlling scheme and reasonable division of responsibilities in voltage sag-associated accidents. Existing methods for classifying voltage sag sources usually ignore th
Externí odkaz:
https://doaj.org/article/1add1013923141fe96fa1a9d8ab87638
Autor:
Li Yijin, Xia Shanhong
Publikováno v:
Open Chemistry, Vol 19, Iss 1, Pp 1164-1170 (2021)
In order to decrease the difficulty in trace mercury determination, an adsorption method for trace mercury based on Bacillus subtilis cells was proposed in this article. The adsorption process was characterized by optical microscopy and SEM. The adso
Externí odkaz:
https://doaj.org/article/c0348a48609f4131a3499a8f770a8d2c
Autor:
Dong, Yitong, Li, Yijin, Huang, Zhaoyang, Bian, Weikang, Liu, Jingbo, Bao, Hujun, Cui, Zhaopeng, Li, Hongsheng, Zhang, Guofeng
In this paper, we propose a novel multi-view stereo (MVS) framework that gets rid of the depth range prior. Unlike recent prior-free MVS methods that work in a pair-wise manner, our method simultaneously considers all the source images. Specifically,
Externí odkaz:
http://arxiv.org/abs/2411.01893
Autor:
Ni, Junjie, Zhang, Guofeng, Li, Guanglin, Li, Yijin, Liu, Xinyang, Huang, Zhaoyang, Bao, Hujun
We tackle the efficiency problem of learning local feature matching. Recent advancements have given rise to purely CNN-based and transformer-based approaches, each augmented with deep learning techniques. While CNN-based methods often excel in matchi
Externí odkaz:
http://arxiv.org/abs/2410.22733
Autor:
Li, Yijin, Shen, Yichen, Huang, Zhaoyang, Chen, Shuo, Bian, Weikang, Shi, Xiaoyu, Wang, Fu-Yun, Sun, Keqiang, Bao, Hujun, Cui, Zhaopeng, Zhang, Guofeng, Li, Hongsheng
Recent advances in event-based vision suggest that these systems complement traditional cameras by providing continuous observation without frame rate limitations and a high dynamic range, making them well-suited for correspondence tasks such as opti
Externí odkaz:
http://arxiv.org/abs/2410.20451
Autor:
Shen, Yichen, Li, Yijin, Chen, Shuo, Li, Guanglin, Huang, Zhaoyang, Bao, Hujun, Cui, Zhaopeng, Zhang, Guofeng
Feature tracking is crucial for, structure from motion (SFM), simultaneous localization and mapping (SLAM), object tracking and various computer vision tasks. Event cameras, known for their high temporal resolution and ability to capture asynchronous
Externí odkaz:
http://arxiv.org/abs/2409.17981
Atomic Force Microscopy (AFM) is a widely employed tool for micro-/nanoscale topographic imaging. However, conventional AFM scanning struggles to reconstruct complex 3D micro-/nanostructures precisely due to limitations such as incomplete sample topo
Externí odkaz:
http://arxiv.org/abs/2401.11541
Different from traditional video cameras, event cameras capture asynchronous events stream in which each event encodes pixel location, trigger time, and the polarity of the brightness changes. In this paper, we introduce a novel graph-based framework
Externí odkaz:
http://arxiv.org/abs/2308.14419
Autor:
Liu, Xinyang, Li, Yijin, Teng, Yanbin, Bao, Hujun, Zhang, Guofeng, Zhang, Yinda, Cui, Zhaopeng
Light-weight time-of-flight (ToF) depth sensors are compact and cost-efficient, and thus widely used on mobile devices for tasks such as autofocus and obstacle detection. However, due to the sparse and noisy depth measurements, these sensors have rar
Externí odkaz:
http://arxiv.org/abs/2308.14383