Deterministic Fitting of Multiple Structures Using Iterative MaxFS with Inlier Scale Estimation
Kwang Hee Lee,Sang-Wook Lee +1 more
- 01 Dec 2013
- pp 41-48
TL;DR: Experimental results demonstrate that the presented algorithm can generate more reliable and consistent hypotheses than random sampling-based methods for estimating multiple structures from data with many outliers and also overcomes the second limitation by reducing data for the MaxFS problem.
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Abstract: 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.
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Citations
Deterministic Fitting of Multiple Structures Using Iterative MaxFS with Inlier Scale Estimation
Kwang Hee Lee,Sang-Wook Lee +1 more
- 01 Dec 2013
TL;DR: Experimental results demonstrate that the presented algorithm can generate more reliable and consistent hypotheses than random sampling-based methods for estimating multiple structures from data with many outliers and also overcomes the second limitation by reducing data for the MaxFS problem.
•Proceedings Article
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Wang Zhao,Shaohui Liu,Yi Wei,Hengkai Guo,Yong-Jin Liu +4 more
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Superpixel-Guided Two-View Deterministic Geometric Model Fitting
TL;DR: Zhang et al. as mentioned in this paper proposed a superpixel-guided two-view geometric model fitting method (SDF), which includes three main parts: a deterministic sampling algorithm, a model hypothesis updating strategy and a novel model selection algorithm.
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Practical Motion Segmentation for Urban Street View Scenes
Cosimo Rubino,Alessio Del Bue,Tat-Jun Chin +2 more
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TL;DR: A new approach for image-based motion segmentation in the case of vehicles navigating inside an urban environment by exploiting two application-specific factors and constraining the geometry and exploiting known semantic classes in the scene achieves much higher accuracy than previous approaches.
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The maximum feasible subset problem (maxFS) and applications
TL;DR: There is a surprisingly large range of applications for algorithms that solve the linear maxFS problem, including analyzing infeasible linear programs, finding the data depth, placing separating hyperplanes in classification decision trees, recovering sparse data in compressed sensing, dimension reduction in nonnegative matrix factorization, etc.
10
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