基于特征距离与内点的随机抽样一致性算法

doi:10.11999/JEIT170703

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Abstract In order to improve the operation efficiency of the RANdom SAmple Consensus (RANSAC) in feature registration, the Random Sample Consensus based on Feature Distance and Inliers (RSCFDI) is proposed. Firstly, the priori probability guidance method based on feature distance is proposed for increasing the probability of searching correct model in each loop. Then, to increase the convergence rate, the random sampling and calculation method based on sample set and inliers is adopted. Finally, the jumping out loop based on unchanged maximum is proposed to fit the proposed loop breaking, and the execution speed is elevated. The theoretical proof and experimental results show that the RSCFDI ensures the robustness of the algorithm, and the operation efficiency is improved.

Key words： Model estimation RANdom SAmple Consensus (RANSAC) Feature distance Inliers sample Homograph matrix

Received: 17 July 2017 Published: 29 January 2018

PACS:

TP391.4

Corresponding Authors: ZHANG Yan E-mail: hillwind@126.com

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	ZHANG Yan
	SUN Shiyu
	HU Yongjiang
	LI Jianzeng
	FAN Cong

Cite this article:

ZHANG Yan,SUN Shiyu,HU Yongjiang等. Random Sample Consensus Algorithm Based on Feature Distance and Inliers[J]. JEIT, 2018, 40(4): 928-935.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT170703 OR http://jeit.ie.ac.cn/EN/Y2018/V40/I4/928

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