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SALIENCY DETECTION VIA LOCAL SINGLE GAUSSIAN MODEL
会议论文
2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2017-01-01
作者:
Xu, Nan
;
Guo, Yanqing
;
Kong, Xiangwei
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  |  
浏览/下载:1/0
  |  
提交时间:2019/12/03
Bottom-up model
local single Gaussian model
saliency map
Efficient Moving Object Segmentation Algorithm Based on the Improvement of Generalized Geodesic Active Contour Model
会议论文
作者:
Chen, Ying
;
Yu, Qi
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  |  
浏览/下载:6/0
  |  
提交时间:2019/12/02
moving object segmentation
generalized geodesic active contour model
level set function
single gaussian model
Image database retrieval using GMM-AIB framework
期刊论文
Journal of Computational Information Systems, 2012, 卷号: 8, 期号: 22, 页码: 9141-9149
作者:
Li, Junyi
;
Li, Jianhua
;
Yan, Shuicheng
;
Wang, Gang
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  |  
浏览/下载:2/0
  |  
提交时间:2019/12/23
AIB clustering
Ensemble learning
Gaussian mixed model
Image representation
Multi-instance learning
Scene recognition
Single-instance bag
The random electrode selection ensemble for EEG signal classification
期刊论文
2010, 2010
Sun, Shiliang
;
Zhang, Changshui
;
Lu, Yue
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浏览/下载:2/0
An information theoretic approach to model reduction based on frequency-domain Cross-Gramian information (EI CONFERENCE)
会议论文
2010 8th World Congress on Intelligent Control and Automation, WCICA 2010, July 7, 2010 - July 9, 2010, Jinan, China
Fu J.
;
Zhong C.
;
Ding Y.
;
Zhou J.
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浏览/下载:23/0
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提交时间:2013/03/25
We focused our attention on model reduction for linear time invariant (LTI) continuous time systems with single input and single output (SISO). We analyzed the strongpoint and shortcoming of an information theoretic approach called minimum information loss method for model reduction. We explained relationships between the steady-state information entropy and controllability information for the Gaussian systems. The controllability
observability and Cross-Gramian information were analyzed in the time-domain. The frequency-domain Cross-Gramian information (FCGI) was defined based on Cross-Gramian matrix containing information associated with both controllability and observability. Furthermore
a valuable application of the frequency-domain Cross-Gramian information to model reduction (FCGMIL) was developed by using the concept and properties of frequency-domain Cross-Gramian information. The performance index of FCGMIL is to minimize the frequency-domain Cross-Gramian information loss caused by eliminating the state variables with weak contributions to Cross-Gramian information over some frequency band. Two numerical examples are illustrated to verify the efficiency of FCGMIL. 2010 IEEE.
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