Random sampling and model competition for guaranteed multiple consensus sets estimation
Date
2017-01-02
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Sage Publications Inc.
Abstract
Robust extraction of consensus sets from noisy data is a fundamental problem in robot vision. Existing multimodel
estimation algorithms have shown success on large consensus sets estimations. One remaining challenge is to extract
small consensus sets in cluttered multimodel data set. In this article, we present an effective multimodel extraction
method to solve this challenge. Our technique is based on smallest consensus set random sampling, which we prove can
guarantee to extract all consensus sets larger than the smallest set from input data. We then develop an efficient model
competition scheme that iteratively removes redundant and incorrect model samplings. Extensive experiments on both
synthetic data and real data with high percentage of outliers and multimodel intersections demonstrate the superiority of
our method.
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Citation
Li, Jing, Tao Yang, and Jingyi Yu. "Random sampling and model competition for guaranteed multiple consensus sets estimation." International Journal of Advanced Robotic Systems 14, no. 1 (2017): 1729881416685673.