中文版 | English

在结果中检索

CASIA OpenIR
（本次检索基于用户作品认领结果）

浏览/检索结果: 共5条，第1-5条 帮助

限定条件            
已选(0)清除 条数/页：   排序方式：
Cross stage partial connections based weighted Bi-directional feature pyramid and enhanced spatial transformation network for robust object detection 期刊论文
NEUROCOMPUTING, 2022, 卷号: 513, 页码: 70-82
作者:  Lu, Yan-Feng;  Yu, Qian;  Gao, Jing-Wen;  Li, Yi;  Zou, Jun-Cheng;  Qiao, Hong
Adobe PDF(3025Kb)  |  收藏  |  浏览/下载：246/7  |  提交时间：2022/11/14
Robust object detection  Structural deformation  Image detection  Spatial transformation  
Recent Advances on Application of Deep Learning for Recovering Object Pose 会议论文
IEEE International Conference on Robotics and Biomimetics, Qingdao, China, Dec. 3 – Dec. 7, 2016
作者:  Li, Wanyi;  Luo, Yongkang;  Wang, Peng;  Qin, Zhengke;  Zhou, Hai;  Qiao, Hong
Adobe PDF(712Kb)  |  收藏  |  浏览/下载：578/263  |  提交时间：2016/10/16
Pose Estimation  Deep Learning  Survey  
Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2016, 卷号: 46, 期号: 10, 页码: 2335-2347
作者:  Qiao, Hong;  Li, Yinlin;  Li, Fengfu;  Xi, Xuanyang;  Wu, Wei
Adobe PDF(2781Kb)  |  收藏  |  浏览/下载：469/155  |  提交时间：2016/06/21
Biologically Inspired  Hierarchical Model  Key Components Learning  Semantic Description  
Introducing Memory and Association Mechanism Into a Biologically Inspired Visual Model 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2014, 卷号: 44, 期号: 9, 页码: 1485-1496
作者:  Qiao, Hong;  Li, Yinlin;  Tang, Tang;  Wang, Peng
Adobe PDF(13245Kb)  |  收藏  |  浏览/下载：299/51  |  提交时间：2015/08/12
Association  Biologically Inspired Visual Model  Memory  Object Recognition  
Sensor-less insertion strategy for an eccentric peg in a hole of the crankshaft and bearing assembly 期刊论文
ASSEMBLY AUTOMATION, 2012, 卷号: 32, 期号: 1, 页码: 86-99
作者:  Su, Jianhua;  Qiao, Hong;  Ou, Zhicai;  Zhang, Yuren
Adobe PDF(731Kb)  |  收藏  |  浏览/下载：394/100  |  提交时间：2015/08/12
Eccentric Peg-in-hole  Robotic Assembly  Vision Guided  Attractive Region  Crankshaft And Bearing  Precision Engineering  Automotive Industry