Skip to main content

On Image Reconstruction from Multiscale Top Points

  • Conference paper
Scale Space and PDE Methods in Computer Vision (Scale-Space 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3459))

Included in the following conference series:

  • 1560 Accesses

Abstract

Image reconstruction from a fiducial collection of scale space interest points and attributes (e.g. in terms of image derivatives) can be used to make the amount of information contained in them explicit. Previous work by various authors includes both linear and non-linear image reconstruction schemes. In this paper, the authors present new results on image reconstruction using a top point representation of an image. A hierarchical ordering of top points based on a stability measure is presented, comparable to feature strength presented in various other works. By taking this into account our results show improved reconstructions from top points compared to previous work. The proposed top point representation is compared with previously proposed representations based on alternative feature sets, such as blobs using two reconstruction schemes (one linear, one non-linear). The stability of the reconstruction from the proposed top point representation under noise is also considered.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Brox, T., Weickert, J.: A tv flow based local scale measure for texture discrimination. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3022, pp. 578–590. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  2. Janssen, B.J., Kanters, F.M.W., Duits, R., Florack, L.M.J.: A linear image reconstruction framework based on Sobolev type inner products. In: Kimmel, R., Sochen, N.A., Weickert, J. (eds.) Scale-Space 2005. LNCS, vol. 3459, pp. 85–96. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  3. Kanters, F.M.W., Platel, B., Florack, L.M.J., ter Haar Romeny, B.M.: Image reconstruction from multiscale critical points. In: Griffin, L.D., Lillholm, M. (eds.) Scale-Space 2003. LNCS, vol. 2695, pp. 464–478. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  4. Kuijper, A., Florack, L.M.J.: Hierarchical pre-segmentation without prior knowledge. In: Proceedings of the 8th International Conference on Computer Vision, Vancouver, Canada, July 9–12, pp. 487–493. IEEE Computer Society Press, Los Alamitos (2001)

    Google Scholar 

  5. Lillholm, M., Nielsen, M., Griffin, L.D.: Feature-based image analysis. International Journal of Computer Vision 52(2/3), 73–95 (2003)

    Article  Google Scholar 

  6. Lindeberg, T.: Feature detection with automatic scale selection. International Journal of Computer Vision 30(2), 79–116 (1998)

    Article  Google Scholar 

  7. Nielsen, M., Lillholm, M.: What do features tell about images? In: Kerckhove, M. (ed.) Scale-Space 2001. LNCS, vol. 2106, pp. 39–50. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  8. Platel, B., Kanters, F.M.W., Florack, L.M.J., Balmachnova, E.G.: Using multiscale top points in image matching. In: Proceedings of the 11th international conference on Image Processing, Singapore (October 2004)

    Google Scholar 

  9. Tikhonov, A., Arseninn, V.Y.: Solution of Ill-Posed Problems. John Wiley & Sons, New York (1977)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kanters, F. et al. (2005). On Image Reconstruction from Multiscale Top Points. In: Kimmel, R., Sochen, N.A., Weickert, J. (eds) Scale Space and PDE Methods in Computer Vision. Scale-Space 2005. Lecture Notes in Computer Science, vol 3459. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11408031_37

Download citation

  • DOI: https://doi.org/10.1007/11408031_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25547-5

  • Online ISBN: 978-3-540-32012-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics