Quasi-maximum feasible subsystem for geometric computer vision problems

Chanki Yu, Da Young Ju, Sang Wook Lee

Research output: Contribution to journalArticle

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 languageEnglish
Pages (from-to)1071-1073
Number of pages3
JournalElectronics Letters
Volume51
Issue number14
DOIs
Publication statusPublished - 2015 Jan 1

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Computer vision
Set theory
Parameter estimation

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Yu, Chanki ; Ju, Da Young ; Lee, Sang Wook. / Quasi-maximum feasible subsystem for geometric computer vision problems. In: Electronics Letters. 2015 ; Vol. 51, No. 14. pp. 1071-1073.
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Quasi-maximum feasible subsystem for geometric computer vision problems. / Yu, Chanki; Ju, Da Young; Lee, Sang Wook.

In: Electronics Letters, Vol. 51, No. 14, 01.01.2015, p. 1071-1073.

Research output: Contribution to journalArticle

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