10 April 2018 An adaptive clustering algorithm for image matching based on corner feature
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106151S (2018) https://doi.org/10.1117/12.2304513
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
Abstract
The traditional image matching algorithm always can not balance the real-time and accuracy better, to solve the problem, an adaptive clustering algorithm for image matching based on corner feature is proposed in this paper. The method is based on the similarity of the matching pairs of vector pairs, and the adaptive clustering is performed on the matching point pairs. Harris corner detection is carried out first, the feature points of the reference image and the perceived image are extracted, and the feature points of the two images are first matched by Normalized Cross Correlation (NCC) function. Then, using the improved algorithm proposed in this paper, the matching results are clustered to reduce the ineffective operation and improve the matching speed and robustness. Finally, the Random Sample Consensus (RANSAC) algorithm is used to match the matching points after clustering.

The experimental results show that the proposed algorithm can effectively eliminate the most wrong matching points while the correct matching points are retained, and improve the accuracy of RANSAC matching, reduce the computation load of whole matching process at the same time.
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Zhe Wang, Zhe Wang, Min Dong, Min Dong, Xiaomin Mu, Xiaomin Mu, Song Wang, Song Wang, } "An adaptive clustering algorithm for image matching based on corner feature", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106151S (10 April 2018); doi: 10.1117/12.2304513; https://doi.org/10.1117/12.2304513
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