Article ID Journal Published Year Pages File Type
527313 Image and Vision Computing 2010 9 Pages PDF
Abstract

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∞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.

Keywords
Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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