Quasi-maximum feasible subsystem for geometric computer vision problems

Chanki Yu; Da Young Ju; Sang Wook Lee

doi:10.1049/el.2015.0842

Quasi-maximum feasible subsystem for geometric computer vision problems

Chanki Yu, Da Young Ju, Sang Wook Lee

Yonsei Institute of Convergence Technology

Research output: Contribution to journal › Article › peer-review

Abstract

A robust fitting algorithm for geometric computer vision problems under the L∞-norm optimisation framework is presented. It is essentially based on the maximum feasible subsystem (MaxFS) but it overcomes the computational limitation of the MaxFS for large data by finding only a quasi-maximum feasible subset. Experimental results demonstrate that the algorithm removes outliers more effectively than the other parameter estimation methods recently developed when the outlier-to-inlier ratio in a data set is high.

Original language	English
Pages (from-to)	1071-1073
Number of pages	3
Journal	Electronics Letters
Volume	51
Issue number	14
DOIs	https://doi.org/10.1049/el.2015.0842
Publication status	Published - 2015 Jul 9

Bibliographical note

Publisher Copyright:
© The Institution of Engineering and Technology 2015.

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

Access to Document

10.1049/el.2015.0842

Fingerprint Dive into the research topics of 'Quasi-maximum feasible subsystem for geometric computer vision problems'. Together they form a unique fingerprint.

Cite this

@article{b6256bcce12642509899fef8b70f0a07,

title = "Quasi-maximum feasible subsystem for geometric computer vision problems",

abstract = "A robust fitting algorithm for geometric computer vision problems under the L∞-norm optimisation framework is presented. It is essentially based on the maximum feasible subsystem (MaxFS) but it overcomes the computational limitation of the MaxFS for large data by finding only a quasi-maximum feasible subset. Experimental results demonstrate that the algorithm removes outliers more effectively than the other parameter estimation methods recently developed when the outlier-to-inlier ratio in a data set is high.",

author = "Chanki Yu and Ju, {Da Young} and Lee, {Sang Wook}",

note = "Publisher Copyright: {\textcopyright} The Institution of Engineering and Technology 2015.",

year = "2015",

month = jul,

day = "9",

doi = "10.1049/el.2015.0842",

language = "English",

volume = "51",

pages = "1071--1073",

journal = "Electronics Letters",

issn = "0013-5194",

publisher = "Institution of Engineering and Technology",

number = "14",

}

TY - JOUR

T1 - Quasi-maximum feasible subsystem for geometric computer vision problems

AU - Yu, Chanki

AU - Ju, Da Young

AU - Lee, Sang Wook

PY - 2015/7/9

Y1 - 2015/7/9

N2 - A robust fitting algorithm for geometric computer vision problems under the L∞-norm optimisation framework is presented. It is essentially based on the maximum feasible subsystem (MaxFS) but it overcomes the computational limitation of the MaxFS for large data by finding only a quasi-maximum feasible subset. Experimental results demonstrate that the algorithm removes outliers more effectively than the other parameter estimation methods recently developed when the outlier-to-inlier ratio in a data set is high.

AB - A robust fitting algorithm for geometric computer vision problems under the L∞-norm optimisation framework is presented. It is essentially based on the maximum feasible subsystem (MaxFS) but it overcomes the computational limitation of the MaxFS for large data by finding only a quasi-maximum feasible subset. Experimental results demonstrate that the algorithm removes outliers more effectively than the other parameter estimation methods recently developed when the outlier-to-inlier ratio in a data set is high.

UR - http://www.scopus.com/inward/record.url?scp=84936852426&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84936852426&partnerID=8YFLogxK

U2 - 10.1049/el.2015.0842

DO - 10.1049/el.2015.0842

M3 - Article

AN - SCOPUS:84936852426

VL - 51

SP - 1071

EP - 1073

JO - Electronics Letters

JF - Electronics Letters

SN - 0013-5194

IS - 14

ER -

Quasi-maximum feasible subsystem for geometric computer vision problems

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Cite this