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.

The maximum feasible subset problem (maxFS) and applications

“…MaxFS is an essential part of algorithms for computer vision (Lee and Lee 2013;Yu et al 2015;Chin and Suter 2017;Lee et al 2018). Measurements of various types (e.g.…”