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Research on Sparse Feature Matching of Improved RANSAC Algorithm

机译：改进的RANSAC算法的稀疏特征匹配研究

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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.
机译：提出了一种基于改进RANSAC算法的稀疏特征匹配方法，以提高精度和速度。首先，利用SIFT算法提取图像的特征点。然后，通过生成SIFT特征描述符对图像对进行粗略匹配。最后，通过改进的RANSAC算法优化了图像匹配的精度。从三个方面对RANSAC算法进行了改进：本文以8点算法生成的基本矩阵为模型，代替了单应矩阵。样本采用随机块选择法进行选择，保证了分布的均匀性和准确性。在标准RANSAC的基础上增加了顺序概率比测试（SPRT），从而减少了算法的总体运行时间。实验结果表明，该方法不仅可以获得较高的匹配精度，而且可以大大减少计算量，提高匹配速度。

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