Li, JingYang, TaoYu, Jingyi2018-08-082018-08-08Copyright2017-01-02Li, 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): 1729881416685673.1729-8814http://udspace.udel.edu/handle/19716/23667Publisher's PDFRobust 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.en-USArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.Creative Commons CC-BY: This article is distributed under the terms of the Creative Commons Attribution 3.0 License (http://www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages.Article10.1177/1729881416685673