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 | |
| Publication status | Published - 2015 Jul 9 |
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
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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