Robust Multisensor Image Matching Using Bayesian Estimated Mutual Information

Abstract:

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 estimate 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 Pluims method. However, our computation is far more cost saving.

• Periodical: Applied Mechanics and Materials (Volumes 321-324)
• Pages: 541-548
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.321-324.541
• Online since: June 2013
• Authors: Yu Zhuang Yan, Lu Rong Shen, Yong Bin Zheng, Wan Ying Xu, Xin Sheng Huang
• Keywords: Bayesian, Gradient Orientation, Likelihood Probability, Multisensor Image Matching, Mutual Information (MI), Prior Probability

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