Random sampling and model competition for guaranteed multiple consensus sets estimation

Author(s)Li, Jing
Author(s)Yang, Tao
Author(s)Yu, Jingyi
Ordered AuthorJing Li, Tao Yang and Jingyi Yu
UD AuthorYu, Jingyien_US
Date Accessioned2018-08-08T15:41:44Z
Date Available2018-08-08T15:41:44Z
Copyright DateCopyright © The Author(s) 2017.en_US
Publication Date2017-01-02
DescriptionPublisher's PDFen_US
AbstractRobust 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_US
DepartmentUniversity of Delaware. Department of Computer and Information Sciences.en_US
CitationLi, 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.en_US
DOI10.1177/1729881416685673en_US
ISSN1729-8814en_US
URLhttp://udspace.udel.edu/handle/19716/23667
Languageen_USen_US
PublisherSage Publications Inc.en_US
dc.rightsArticle 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.en_US
dc.rightsCreative 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.en_US
dc.sourceInternational Journal of Advanced Robotic Systemsen_US
dc.source.urihttp://journals.sagepub.com/home/arxen_US
TitleRandom sampling and model competition for guaranteed multiple consensus sets estimationen_US
TypeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Random sampling and model competition for guaranteed multiple consensus sets estimation_1492793342T6116.pdf
Size:
4.75 MB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.22 KB
Format:
Item-specific license agreed upon to submission
Description: