KAGO: an approximate adaptive grid-based outlier detection approach using kernel density estimate

Author(s):  
Panthadeep Bhattacharjee ◽  
Ankur Garg ◽  
Pinaki Mitra
2013 ◽  
Vol 2013 ◽  
pp. 1-11
Author(s):  
Lurong Shen ◽  
Xinsheng Huang ◽  
Yuzhuang Yan ◽  
Yongbin Zheng ◽  
Wanying Xu

Mutual information (MI) has been widely used in multisensor image matching, but it may lead to mismatch among images with messy background. However, additional prior information can be of great help in improving the matching performance. In this paper, a robust Bayesian estimated mutual information, named as BMI, for multisensor image matching is proposed. This method has been implemented by utilizing the gradient prior information, in which the prior is estimated by the kernel density estimate (KDE) method, and the likelihood is modeled according to the distance of orientations. To further improve the robustness, we restrict the matching within the regions where the corresponding pixels of template image are salient enough. Experiments on several groups of multisensor images show that the proposed method outperforms the standard MI in robustness and accuracy and is similar with Pluim’s method. However, our computation is far more cost saving.


1999 ◽  
Vol 44 (3) ◽  
pp. 299-308 ◽  
Author(s):  
Luc Devroye ◽  
Adam Krzyżak

1993 ◽  
Vol 3 (1) ◽  
pp. 1-11 ◽  
Author(s):  
G.M. El-Sayyad ◽  
M. Samiuddin ◽  
A.A. Abdel-Ghaly

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