2010 | OriginalPaper | Buchkapitel
L-Infinity Norm Minimization in the Multiview Triangulation
verfasst von : Yang Min
Erschienen in: Artificial Intelligence and Computational Intelligence
Verlag: Springer Berlin Heidelberg
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Triangulation is an important part of numerous computer vision systems. The multiview triangulation problem is often solved by minimizing a cost function that combines the reprojection errors in the 2D images. In this paper, we show how to recast multiview triangulation as quasi-convex optimization under the L-infinity norm. It is shown that the L-infinity norm cost function is significantly simpler than the L2 cost. In particular L-infinity norm minimization involves finding the minimum of a cost function with a single global minimum on a convex parameter domain. These problems can be efficiently solved using second-order cone programming. We carried out experiment with real data to show that L-infinity norm minimization provides a more accurate estimate and superior to previous approaches.