Abstract
For improving the accuracy of the SIFT matching algorithm with low time cost, this paper proposes a novel matching algorithm which is based on local neighborhood constraints, that is, SIFT matching feature is optimized by the local neighborhood constraint method in the SIFT algorithm. We optimize the matching results by using the information of SIFT feature descriptor and the relative position information of SIFT feature, then the final matching result obtained by RANSANC algorithm to filter the false matched pairs. The experimental results show that our method can improve the accuracy of the matching feature pairs without affecting the time cost.
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Acknowledgements
This work was supported by the Science & Technology Development Program of Jilin Province, China (Nos. 20140101182JC, 20150101060JC, 20150307030GX, 2015Y059 and 20160204048GX), and by the International Science and Technology Cooperation Program of China (Nos. 20140101182JC, 20150101060JC, 20150307030GX and 20160204048GX).
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Li, Q., Xu, L., Zheng, P., He, F. (2018). A Local Neighborhood Constraint Method for SIFT Features Matching. In: Tavana, M., Patnaik, S. (eds) Recent Developments in Data Science and Business Analytics. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-72745-5_34
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DOI: https://doi.org/10.1007/978-3-319-72745-5_34
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