Title | ||
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Deterministic Fitting of Multiple Structures Using Iterative MaxFS with Inlier Scale Estimation |
Abstract | ||
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We present an efficient deterministic hypothesis generation algorithm for robust fitting of multiple structures based on the maximum feasible subsystem (MaxFS) framework. Despite its advantage, a global optimization method such as MaxFS has two main limitations for geometric model fitting. First, its performance is much influenced by the user-specified inlier scale. Second, it is computationally inefficient for large data. The presented algorithm, called iterative MaxFS with inlier scale (IMaxFS-ISE), iteratively estimates model parameters and inlier scale and also overcomes the second limitation by reducing data for the MaxFS problem. The IMaxFS-ISE algorithm generates hypotheses only with top-n ranked subsets based on matching scores and data fitting residuals. This reduction of data for the MaxFS problem makes the algorithm computationally realistic. A sequential "fitting-and-removing" procedure is repeated until overall energy function does not decrease. Experimental results demonstrate that our method can generate more reliable and consistent hypotheses than random sampling-based methods for estimating multiple structures from data with many outliers. |
Year | DOI | Venue |
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2013 | 10.1109/ICCV.2013.12 | ICCV |
Keywords | Field | DocType |
deterministic fitting,iterative maxfs,inlier scale estimation,large data,user-specified inlier scale,inlier scale,geometric model fitting,robust fitting,maxfs problem,multiple structure,data fitting residual,imaxfs-ise algorithm,iterative methods,computer vision | Mathematical optimization,Global optimization,Curve fitting,Ranking,Computer science,Iterative method,Geometric modeling,Outlier,Scale estimation,Sampling (statistics) | Conference |
Volume | Issue | ISSN |
2013 | 1 | 1550-5499 |
Citations | PageRank | References |
8 | 0.44 | 20 |
Authors | ||
2 |
Name | Order | Citations | PageRank |
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Kwang-Hee Lee | 1 | 17 | 6.37 |
Sang Wook Lee | 2 | 592 | 69.81 |