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Disturbance Rejection Control for Underwater Free-Floating Manipulation 期刊论文
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2022, 卷号: 27, 期号: 5, 页码: 3742 - 3750
作者:  Lv, Jiaqi;  Wang, Yu;  Tang, Chong;  Wang, Shuo;  Xu, Wenxing;  Wang, Rui;  Tan, Min
Adobe PDF(10105Kb)  |  收藏  |  浏览/下载：312/32  |  提交时间：2022/01/27
Disturbance observer-based control (DOBC)  fast arctangent nonsingularity terminal sliding mode controller (FANTSMC)  underwater biomimetic vehicle-manipulator system (UBVMS)  underwater manipulation  Estimation  
Hydrodynamics of a Flexible Flipper for an Underwater Vehicle-Manipulator System 期刊论文
IEEE/ASME Transactions on Mechatronics, 2021, 卷号: -, 期号: -, 页码: 1-11
作者:  Bai XJ(白雪剑);  Wang Y(王宇);  Wang R(王睿);  Wang S(王硕);  Tan M(谭民)
Adobe PDF(8510Kb)  |  收藏  |  浏览/下载：192/46  |  提交时间：2022/01/06
Biomimetic robot  flexible flipper  hydrodynamic model  underwater vehicle-manipulator system  
Real-Time Multi-Guidewire Endpoint Localization in Fluoroscopy Images 期刊论文
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2021, 卷号: 40, 期号: 8, 页码: 2002-2014
作者:  Li, Rui-Qi;  Xie, Xiao-Liang;  Zhou, Xiao-Hu;  Liu, Shi-Qi;  Ni, Zhen-Liang;  Zhou, Yan-Jie;  Bian, Gui-Bin;  Hou, Zeng-Guang
Adobe PDF(2266Kb)  |  收藏  |  浏览/下载：353/49  |  提交时间：2021/11/02
Location awareness  Surgery  Instruments  Task analysis  Detectors  Real-time systems  Retina  Guidewire endpoint  keypoint localization  surgical instrument  
Multiple object tracking: A literature review 期刊论文
ARTIFICIAL INTELLIGENCE, 2021, 卷号: 293, 页码: 23
作者:  Luo, Wenhan;  Xing, Junliang;  Milan, Anton;  Zhang, Xiaoqin;  Liu, Wei;  Kim, Tae-Kyun
收藏  |  浏览/下载：127/0  |  提交时间：2021/04/06
Multi-object tracking  Data association  Survey  
Robot learning through observation via coarse-to-fine grained video summarization 期刊论文
APPLIED SOFT COMPUTING, 2021, 卷号: 99, 期号: /, 页码: 106913
作者:  Zhang, Yujia;  Li, Qianzhong;  Zhao, Xiaoguang;  Tan, Min
Adobe PDF(5989Kb)  |  收藏  |  浏览/下载：352/73  |  提交时间：2021/03/08
Robotic vision  Learning through observation  Coarse-to-fine video summarization  
Improve Person Re-Identification With Part Awareness Learning 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 页码: 7468-7481
作者:  Huang, Houjing;  Yang, Wenjie;  Lin, Jinbin;  Huang, Guan;  Xu, Jiamiao;  Wang, Guoli;  Chen, Xiaotang;  Huang, Kaiqi
Adobe PDF(3927Kb)  |  收藏  |  浏览/下载：316/56  |  提交时间：2020/08/31
Person re-identification  part awareness  part segmentation  multi-task learning  
Joint Anchor-Feature Refinement for Real-Time Accurate Object Detection in Images and Videos 期刊论文
IEEE Transactions on Circuits and Systems for Video Technology, 2020, 卷号: 无, 期号: 无, 页码: 无
作者:  Chen, Xingyu;  Yu, Junzhi;  Kong, Shihan;  Wu, Zhengxing;  Wen, Li
浏览  |  Adobe PDF(4122Kb)  |  收藏  |  浏览/下载：237/57  |  提交时间：2020/06/08
Object detection  Neural networks  Computer vision  Deep learning  
Coordinated Control of Underwater Biomimetic Vehicle-Manipulator System for Free Floating Autonomous Manipulation 期刊论文
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 卷号: 51, 期号: 无, 页码: 无
作者:  Cai, Mingxue;  Wang, Shuo;  Wang, Yu;  Wang, Rui;  Tan, Min
Adobe PDF(2380Kb)  |  收藏  |  浏览/下载：291/71  |  提交时间：2020/05/07
Adaptive Tracking Differentiator (Atd)  Nonsingular Terminal Sliding-mode Control (Ntsmc)  Underwater Autonomous Manipulation  Vehicle–manipulator Coordinated Contron  
Recurrent Prediction with Spatio-temporal Attention for Crowd Attribute Recognition 期刊论文
IEEE Transactions on Circuits and Systems for Video Technology, 2019, 卷号: 30, 期号: Early Access, 页码: 1 - 1
作者:  Li, Qiaozhe;  Zhao, Xin;  He, Ran;  Huang, Kaiqi
浏览  |  Adobe PDF(2648Kb)  |  收藏  |  浏览/下载：395/104  |  提交时间：2020/01/14
Crowd video understanding , Attribute recognition , Attention mechanism , Multi-label classification  
Focal Boundary Guided Salient Object Detection 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2019, 卷号: 28, 期号: 6, 页码: 2813-2824
作者:  Wang, Yupei;  Zhao, Xin;  Hu, Xuecai;  Li, Yin;  Huang, Kaiqi
浏览  |  Adobe PDF(3275Kb)  |  收藏  |  浏览/下载：583/263  |  提交时间：2019/04/19
Visual saliency detection  salient object segmentation  boundary detection  deep learning