Advertisement

Invariant Shape Matching for Detection of Semi-local Image Structures

  • Lech Szumilas
  • Horst Wildenauer
  • Allan Hanbury
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5627)

Abstract

Shape features applied to object recognition has been actively studied since the beginning of the field in 1950s and remain a viable alternative to appearance based methods e.g. local descriptors. This work address the problem of learning and detecting repeatable shape structures in images that may be incomplete, contain noise and/or clutter as well as vary in scale and orientation. A new approach is proposed where invariance to image transformations is obtained through invariant matching rather than typical invariant features. This philosophy is especially applicable to shape features such as open edges which do not have a specific scale or specific orientation until assembled into an object. Our primary contributions are: a new shape-based image descriptor that encodes a spatial configuration of edge parts, a technique for matching descriptors that is rotation and scale invariant and shape clustering that can extract frequently appearing image structures from training images without a supervision.

Keywords

Shape features image descriptor model extraction 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Donner, R., Micusik, B., Langs, G., Szumilas, L., Peloschek, P., Friedrich, K., Bischof, H.: Object localization based on markov random fields and symmetry interest points. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 460–468. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley Interscience, Hoboken (2000)zbMATHGoogle Scholar
  3. 3.
    Leibe, B., Leonardis, A., Schiele, B.: Combined object categorization and segmentation with an implicit shape model. In: ECCV 2004 Workshop on Statistical Learning in Computer Vision, Prague, Czech Republic, pp. 17–32 (2004)Google Scholar
  4. 4.
    Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. International Journal of Computer Vision 60(1), 63–86 (2004)CrossRefGoogle Scholar
  5. 5.
    Zhang, J., Marszałek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study. International Journal of Computer Vision 73(2), 213–238 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Lech Szumilas
    • 1
  • Horst Wildenauer
    • 1
  • Allan Hanbury
    • 1
  1. 1.Vienna University of TechnologyWienAustria

Personalised recommendations