中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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CAS IR Grid
机构
自动化研究所 [3]
国家天文台 [1]
西安光学精密机械研究... [1]
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OAI收割 [5]
内容类型
期刊论文 [5]
发表日期
2024 [1]
2018 [2]
2016 [1]
2015 [1]
学科主题
CIVIL ENGI... [1]
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Robust HDR reconstruction using 3D patch based on two-scale decomposition
期刊论文
OAI收割
Signal Processing, 2024, 卷号: 219
作者:
Qiao, Zhangchi
;
Yi, Hongwei
;
Wen, Desheng
;
Han, Yong
  |  
收藏
  |  
浏览/下载:23/0
  |  
提交时间:2024/03/07
High dynamic range imaging
Multi-exposure image fusion
Two-scale features
Patch-based match
Dynamic/multi-view scene
On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization
期刊论文
OAI收割
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2018, 卷号: 126, 期号: 11, 页码: 1157-1179
作者:
Xie, Yuan
;
Tao, Dacheng
;
Zhang, Wensheng
;
Liu, Yan
;
Zhang, Lei
  |  
收藏
  |  
浏览/下载:53/0
  |  
提交时间:2019/12/16
T-svd
Tensor Multi-rank
Multi-view Features
Subspace Clustering
Driver State Analysis Based on Imperfect Multi-view Evidence Support
期刊论文
OAI收割
NEURAL PROCESSING LETTERS, 2018, 卷号: 48, 期号: 1, 页码: 195-217
作者:
Du, Yong
;
Wang, Yu
;
Huang, Xin
;
Hu, Qinghua
  |  
收藏
  |  
浏览/下载:20/0
  |  
提交时间:2020/03/10
Driver state analysis
Fuzzy rough sets
Multi-view features fusion
Feature selection
Models integration
A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications
期刊论文
OAI收割
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2016, 卷号: 65, 期号: 6, 页码: 4144-4158
作者:
Wang, Kunfeng
;
Liu, Yuqiang
;
Gou, Chao
;
Wang, Fei-Yue
;
Wang, Kunfeng(王坤峰)
收藏
  |  
浏览/下载:43/0
  |  
提交时间:2016/04/06
Conditional independence
foreground detection
heterogeneous features
Markov random field (MRF)
multi-view learning
Image automatic annotation via multi-view deep representation
期刊论文
OAI收割
Journal of Visual Communication and Image Representation, 2015, 卷号: 2015, 期号: 33, 页码: 368-377
作者:
Yang Y(杨阳)
;
Zhang Wensheng(张文生)
;
Xie Yuan
;
wensheng zhang
  |  
收藏
  |  
浏览/下载:25/0
  |  
提交时间:2016/10/13
Image Annotation
Stacked Auto-encoder
Imbalance Learning
Multi-view Learning
Image Features
Semantic Gap
Deep Learning
Multi-labeling