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Rank estimation in 3D multibody motion segmentation

Rank estimation in 3D multibody motion segmentation

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A novel technique for rank estimation in 3D multibody motion segmentation is proposed. It is based on the study of the frequency spectra of moving rigid objects and does not use or assume a prior knowledge of the objects contained in the scene (i.e. number of objects and motion). The significance of rank estimation on multibody motion segmentation results is shown by using two motion segmentation algorithms over both synthetic and real data.

References

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      • Tron, R., Vidal, R.: `A benchmark for the comparison of 3-D motion segmentation algorithms', IEEE Int. Conf. on Computer Vision and Pattern Recognition, June 2007, Minneapolis, MN, USA, p. 1–8.
    2. 2)
      • Vidal, R., Hartley, R.: `Motion segmentation with missing data using Powerfactorisation and GPCA', IEEE Int. Conf. on Computer Vision and Pattern Recognition, June 2004, Washington, DC, USA, II, p. 310–316.
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      • Buchanan, A., Fitzgibbon, A.W.: `Damped Newton algorithms for matrix factorisation with missing data', IEEE Int. Conf. on Computer Vision and Pattern Recognition, June 2005, San Diego, CA, USA, II, p. 316–322.
    4. 4)
      • Yan, J., Pollefeys, M.: `A general framework for motion segmentation: independent, articulated, rigid, non-rigid, degenerate and non-degenerate', European Conf. on Computer Vision, May 2006, Austria, p. 94–106.
http://iet.metastore.ingenta.com/content/journals/10.1049/el_20082503
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