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Xiangsi Kong

at BUCEA

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Proceedings Article | 10 April 2018 Paper

Research on sparse feature matching of improved RANSAC algorithm

Xiangsi Kong, Xian Zhao

Proceedings Volume 10615, 106154O (2018) https://doi.org/10.1117/12.2302707

KEYWORDS: Data modeling, Statistical modeling, Cameras, Feature extraction, Image processing, Error analysis, 3D modeling, Detection and tracking algorithms, Imaging systems, Geomatics

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In this paper, a sparse feature matching method based on modified RANSAC algorithm is proposed to improve the precision and speed. Firstly, the feature points of the images are extracted using the SIFT algorithm. Then, the image pair is matched roughly by generating SIFT feature descriptor. At last, the precision of image matching is optimized by the modified RANSAC algorithm,. The RANSAC algorithm is improved from three aspects: instead of the homography matrix, this paper uses the fundamental matrix generated by the 8 point algorithm as the model; the sample is selected by a random block selecting method, which ensures the uniform distribution and the accuracy; adds sequential probability ratio test(SPRT) on the basis of standard RANSAC, which cut down the overall running time of the algorithm. The experimental results show that this method can not only get higher matching accuracy, but also greatly reduce the computation and improve the matching speed.

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