Skip to main content

The Space of Multibody Fundamental Matrices: Rank, Geometry and Projection

  • Conference paper
Book cover Dynamical Vision (WDV 2006, WDV 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4358))

Abstract

We study the rank and geometry of the multibody fundamental matrix, a geometric entity characterizing the two-view geometry of dynamic scenes consisting of multiple rigid-body motions. We derive an upper bound on the rank of the multibody fundamental matrix that depends on the number of independent translations. We also derive an algebraic characterization of the SVD of a multibody fundamental matrix in the case of two or odd number of rigid-body motions with a common rotation. This characterization allows us to project an arbitrary matrix onto the space of multibody fundamental matrices using linear algebraic techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Costeira, J., Kanade, T.: Multi-body factorization methods for motion analysis. In: IEEE International Conference on Computer Vision, pp. 1071–1076. IEEE, Los Alamitos (1995)

    Chapter  Google Scholar 

  2. Fitzgibbon, A., Zisserman, A.: Multibody structure and motion: 3D reconstruction of independently moving objects. In: European Conference on Computer Vision, pp. 891–906 (2000)

    Google Scholar 

  3. Harris, J.: Algebraic Geometry: A First Course. Springer, Heidelberg (1992)

    MATH  Google Scholar 

  4. Hartley, R., Vidal, R.: The multibody trifocal tensor: Motion segmentation from 3 perspective views. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 769–775. IEEE, Los Alamitos (2004)

    Google Scholar 

  5. Kanatani, K.: Motion segmentation by subspace separation and model selection. In: IEEE International Conference on Computer Vision, vol. 2, pp. 586–591 (2001)

    Google Scholar 

  6. Kanatani, K.: Evaluation and selection of models for motion segmentation. In: Asian Conference on Computer Vision, pp. 7–12 (2002)

    Google Scholar 

  7. Ma, Y., Soatto, S., Kosecka, J., Sastry, S.: An Invitation to 3D Vision: From Images to Geometric Models. Springer, Heidelberg (2003)

    Google Scholar 

  8. Sturm, P.: Structure and motion for dynamic scenes - the case of points moving in planes. In: European Conference on Computer Vision, pp. 867–882 (2002)

    Google Scholar 

  9. Torr, P., Szeliski, R., Anandan, P.: An integrated Bayesian approach to layer extraction from image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(3), 297–303 (2001)

    Article  Google Scholar 

  10. Torr, P.H.S.: Geometric motion segmentation and model selection. Phil. Trans. Royal Society of London 356(1740), 1321–1340 (1998)

    MATH  MathSciNet  Google Scholar 

  11. Vidal, R., Hartley, R.: Motion segmentation with missing data by PowerFactorization and Generalized PCA. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 310–316. IEEE, Los Alamitos (2004)

    Google Scholar 

  12. Vidal, R., Ma, Y.: A unified algebraic approach to 2-D and 3-D motion segmentation. In: European Conference on Computer Vision, pp. 1–15 (2004)

    Google Scholar 

  13. Vidal, R., Ma, Y., Piazzi, J.: A new GPCA algorithm for clustering subspaces by fitting, differentiating and dividing polynomials. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 510–517. IEEE, Los Alamitos (2004)

    Google Scholar 

  14. Vidal, R., Ma, Y., Soatto, S., Sastry, S.: Two-view multibody structure from motion. International Journal of Computer Vision 68(1), 7–25 (2006)

    Article  Google Scholar 

  15. Vidal, R., Sastry, S.: Optimal segmentation of dynamic scenes from two perspective views. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 281–286. IEEE, Los Alamitos (2003)

    Google Scholar 

  16. Wolf, L., Shashua, A.: Two-body segmentation from two perspective views. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 263–270. IEEE, Los Alamitos (2001)

    Google Scholar 

  17. Wu, Y., Zhang, Z., Huang, T.S., Lin, J.Y.: Multibody grouping via orthogonal subspace decomposition. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 252–257. IEEE, Los Alamitos (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

René Vidal Anders Heyden Yi Ma

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Berlin Heidelberg

About this paper

Cite this paper

Fan, X., Vidal, R. (2007). The Space of Multibody Fundamental Matrices: Rank, Geometry and Projection. In: Vidal, R., Heyden, A., Ma, Y. (eds) Dynamical Vision. WDV WDV 2006 2005. Lecture Notes in Computer Science, vol 4358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70932-9_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-70932-9_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70931-2

  • Online ISBN: 978-3-540-70932-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics