Reformulating the Costeira-Kanade algorithm as a pure mathematical theorem independent of the Tomasi-Kanade factorization, we present a robust segmentation algorithm by incorporating such techniques as dimension correction, model selection using the geometric AIC, and least-median fitting. Doing numerical simulations, we demonstrate that oar algorithm dramatically outperforms existing methods. It does not involve any parameters which need to be adjusted empirically.
Motion segmentation by subspace separation and model selection
Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 ; 2 ; 586-591 vol.2
2001-01-01
649147 byte
Conference paper
Electronic Resource
English
Motion Segmentation by Subspace Separation and Model Selection
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