Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Robust regression and outlier detection
Robust regression and outlier detection
Convex Optimization
Removing Outliers Using The L\infty Norm
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Recovering Camera Motion Using L\infty Minimization
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Quasiconvex Optimization for Robust Geometric Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple-View Geometry Under the {$L_\infty$}-Norm
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper shows that we can classify latent outliers efficiently through the process of minimizing the sum of infeasibilities (SOI). The SOI minimization has been developed in the area of convex optimization to find an initial solution, solve a feasibility problem, or check out some inconsistent constraints. It was also adopted recently as an approximation method to minimize a robust error function under the framework of the L"~ norm minimization for geometric vision problems. In this paper, we show that the SOI minimization is practically effective in collecting outliers when it is applied to geometric vision problems. In particular, this method is useful in structure and motion reconstruction where methods such as RANSAC are not applicable. We demonstrate the effectiveness of the method through experiments with synthetic and real data sets.