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Abstract
The contrast of lunar images is low, and few features can be extracted. Therefore, lunar images can be hardly matched with high accuracy. A lunar image matching method based on features from accelerated segment test (FAST) feature and speeded-up robust features (SURFs) descriptor is presented. First, entropy of image is adopted to automatically compute threshold for extracting FAST features. Second, SURF descriptors are used to describe candidate features, and then initial matches with nearest neighborhood strategy are obtained. Third, outliers are rejected from initial matches by RANSAC-based model estimation strategy and homography constraint. Experimental results show that the proposed method can get enough image correspondences and the matching errors are less than 0.2 pixels. It indicates that the proposed method can automatically achieve high-accuracy lunar image matching and lay good foundation for subsequent lunar image stitching and fusion.
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Authors and Affiliations
Shijiazhuang Campus, Army Engineering University, Shijiazhuang, China
You Zhai, Xiwei Guo, Peng He & Zhuanghe Zhang
Technical Division, 66132 Troops, Beijing, China
Shuai Liu
- You Zhai
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- Shuai Liu
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- Xiwei Guo
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- Peng He
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- Zhuanghe Zhang
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Correspondence toYou Zhai.
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Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA
Qilian Liang
School of Information and Communication Engineering, Dalian University of Technology, Dalian, China
Xin Liu
School of Information Science and Technology, Dalian Maritime University, Dalian, China
Zhenyu Na
College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China
Wei Wang
College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China
Jiasong Mu
College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China
Baoju Zhang
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Zhai, Y., Liu, S., Guo, X., He, P., Zhang, Z. (2020). Lunar Image Matching Based on FAST Features with Adaptive Threshold. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_2
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