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Open AccessArticle

Least Squares Consensus for Matching Local Features

by 1,2,*, 1,* and 3
School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, Guangdong, China
School of Data Science and Information Engineering, Guizhou Minzu University, Guiyang 550025, Guizhou, China
School of Robot Engineering, Yangtze Normal University, Chongqing 408100, Chongqing, China
Authors to whom correspondence should be addressed.
Information 2019, 10(9), 275;
Received: 23 July 2019 / Revised: 28 August 2019 / Accepted: 28 August 2019 / Published: 2 September 2019
This paper presents a new approach to estimate the consensus in a data set. Under the framework of RANSAC, the perturbation on data has not been considered sufficiently. We analysis the computation of homography in RANSAC and find that the variance of its estimation monotonically decreases when the size of sample increases. From this result, we carry out an approach which can suppress the perturbation and estimate the consensus set simultaneously. Different from other consensus estimators based on random sampling methods, our approach builds on the least square method and the order statistics and therefore is an alternative scheme for consensus estimation. Combined with the nearest neighbour-based method, our approach reaches higher matching precision than the plain RANSAC and MSAC, which is shown in our simulations. View Full-Text
Keywords: matching features; least square method; local descriptors; consensus estimation; RANSAC matching features; least square method; local descriptors; consensus estimation; RANSAC
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Zhang, Q.; Shi, B.; Xu, H. Least Squares Consensus for Matching Local Features. Information 2019, 10, 275.

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