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

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

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All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

Cite this

Yu, C., Ju, D. Y., & Lee, S. W. (2015). Quasi-maximum feasible subsystem for geometric computer vision problems. Electronics Letters, 51(14), 1071-1073. https://doi.org/10.1049/el.2015.0842

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Access to Document

10.1049/el.2015.0842