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 |
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All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
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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, 09.07.2015, p. 1071-1073.Research output: Contribution to journal › Article
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 -