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Rank estimation in 3D multibody motion segmentation

Rank estimation in 3D multibody motion segmentation

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© The Institution of Engineering and Technology
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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.

Inspec keywords: image motion analysis; image segmentation; estimation theory

Other keywords: 3D multibody motion segmentation; rank estimation

Subjects: Optical, image and video signal processing; Other topics in statistics; Other topics in statistics; Computer vision and image processing techniques

References

    1. 1)
      • 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.
    3. 3)
      • 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.
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